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Opportunities for Large Language Models and Discourse in Engineering Design (2306.09169v1)

Published 15 Jun 2023 in cs.CL, cs.AI, and cs.CE

Abstract: In recent years, LLMs have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of LLMs have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will LLMs and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as LLMs could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.

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References (57)
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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. 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Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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[2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schaeffer, R., Miranda, B., Koyejo, S.: Are Emergent Abilities of Large Language Models a Mirage? (2023) arXiv:2304.15004 [cs.CL] Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Schick et al. [2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. 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[2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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[2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. 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[2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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[2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. 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[2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Schick et al. [2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. 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[2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. 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[2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. 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Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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[2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schaeffer, R., Miranda, B., Koyejo, S.: Are Emergent Abilities of Large Language Models a Mirage? (2023) arXiv:2304.15004 [cs.CL] Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Schick et al. [2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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[2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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[2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schaeffer, R., Miranda, B., Koyejo, S.: Are Emergent Abilities of Large Language Models a Mirage? (2023) arXiv:2304.15004 [cs.CL] Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Schick et al. [2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schaeffer, R., Miranda, B., Koyejo, S.: Are Emergent Abilities of Large Language Models a Mirage? (2023) arXiv:2304.15004 [cs.CL] Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Schick et al. [2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schaeffer, R., Miranda, B., Koyejo, S.: Are Emergent Abilities of Large Language Models a Mirage? (2023) arXiv:2304.15004 [cs.CL] Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Schick et al. [2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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[2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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[2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. 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Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. 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Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schaeffer, R., Miranda, B., Koyejo, S.: Are Emergent Abilities of Large Language Models a Mirage? (2023) arXiv:2304.15004 [cs.CL] Ouyang et al. [2022] Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P.F., Leike, J., Lowe, R.: Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35, 27730–27744 (2022) Schick et al. [2023] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. 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[2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. 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Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. 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Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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[2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Schick, T., Dwivedi-Yu, J., Dessì, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., Scialom, T.: Toolformer: Language Models Can Teach Themselves to Use Tools (2023) arXiv:2302.04761 [cs.CL] Yao et al. [2023] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., Cao, Y.: ReAct: Synergizing Reasoning and Acting in Language Models (2023) arXiv:2210.03629 [cs.CL] Shinn et al. [2023] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. 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Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. 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Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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[2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shinn, N., Labash, B., Gopinath, A.: Reflexion: an autonomous agent with dynamic memory and self-reflection (2023) arXiv:2303.11366 [cs.AI] Bommasani et al. [2022] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J.Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D.E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P.W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X.L., Li, X., Ma, T., Malik, A., Manning, C.D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J.C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J.S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A.W., Tramèr, F., Wang, R.E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S.M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., Liang, P.: On the Opportunities and Risks of Foundation Models (2022) arXiv:2108.07258 [cs.LG] Regenwetter et al. [2022] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. 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[2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. 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[2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Regenwetter, L., Nobari, A.H., Ahmed, F.: Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design 144(071704) (2022) https://doi.org/10.1115/1.4053859 . Accessed 2023-06-14 Raina et al. [2021] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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[2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Raina, A., Cagan, J., McComb, C.: Design Strategy Network: A Deep Hierarchical Framework to Represent Generative Design Strategies in Complex Action Spaces. Journal of Mechanical Design 144(021404) (2021) https://doi.org/10.1115/1.4052566 . Accessed 2023-06-14 Gyory et al. [2021] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. 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[2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG]
  32. Gyory, J.T., Soria Zurita, N.F., Martin, J., Balon, C., McComb, C., Kotovsky, K., Cagan, J.: Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052488 Gyory et al. [2022] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Gyory, J.T., Kotovsky, K., McComb, C., Cagan, J.: Comparing the Impacts on Team Behaviors Between Artificial Intelligence and Human Process Management in Interdisciplinary Design Teams. Journal of Mechanical Design 144(10) (2022) https://doi.org/10.1115/1.4054723 Sarica et al. [2020] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J., Wood, K.L.: TechNet: Technology semantic network based on patent data. Expert Systems with Applications 142, 112995 (2020) https://doi.org/10.1016/j.eswa.2019.112995 Jang et al. [2021] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Jang, H., Jeong, Y., Yoon, B.: TechWord: Development of a technology lexical database for structuring textual technology information based on natural language processing. Expert Systems with Applications 164, 114042 (2021) https://doi.org/10.1016/j.eswa.2020.114042 Shi et al. [2017] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Shi, F., Chen, L., Han, J., Childs, P.: A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval. Journal of Mechanical Design 139(11) (2017) https://doi.org/10.1115/1.4037649 Sarica and Luo [2021] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. 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[2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Sarica, S., Luo, J.: Stopwords in technical language processing. PLOS ONE 16(8), 0254937 (2021) https://doi.org/10.1371/journal.pone.0254937 Morbach et al. [2009] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Morbach, J., Wiesner, A., Marquardt, W.: OntoCAPE—A (re)usable ontology for computer-aided process engineering. Computers & Chemical Engineering 33(10), 1546–1556 (2009) https://doi.org/10.1016/j.compchemeng.2009.01.019 Booshehri et al. [2021] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Booshehri, M., Emele, L., Flügel, S., Förster, H., Frey, J., Frey, U., Glauer, M., Hastings, J., Hofmann, C., Hoyer-Klick, C., Hülk, L., Kleinau, A., Knosala, K., Kotzur, L., Kuckertz, P., Mossakowski, T., Muschner, C., Neuhaus, F., Pehl, M., Robinius, M., Sehn, V., Stappel, M.: Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis. Energy and AI 5, 100074 (2021) https://doi.org/10.1016/j.egyai.2021.100074 Sanfilippo et al. [2019] Sanfilippo, E.M., Kitamura, Y., Young, R.I.M.: Formal ontologies in manufacturing. Applied Ontology 14(2), 119–125 (2019) https://doi.org/10.3233/AO-190209 Han et al. [2021] Han, J., Sarica, S., Shi, F., Luo, J.: Semantic Networks for Engineering Design: A Survey. Proceedings of the Design Society 1, 2621–2630 (2021) https://doi.org/10.1017/pds.2021.523 . Publisher: Cambridge University Press Siddharth et al. [2021] Siddharth, L., Blessing, L.T.M., Wood, K.L., Luo, J.: Engineering Knowledge Graph From Patent Database. Journal of Computing and Information Science in Engineering 22(2) (2021) https://doi.org/10.1115/1.4052293 Siddharth et al. [2022] Siddharth, L., Blessing, L., Luo, J.: Natural language processing in-and-for design research. Design Science 8, 21 (2022) https://doi.org/10.1017/dsj.2022.16 Zhu and Luo [2022] Zhu, Q., Luo, J.: Generative Pre-Trained Transformer for Design Concept Generation: An Exploration. Proceedings of the Design Society 2, 1825–1834 (2022) https://doi.org/10.1017/pds.2022.185 . Publisher: Cambridge University Press. Accessed 2023-06-14 Zhu and Luo [2023] Zhu, Q., Luo, J.: Generative Transformers for Design Concept Generation. Journal of Computing and Information Science in Engineering 23(4) (2023) https://doi.org/10.1115/1.4056220 Zhu et al. [2023] Zhu, Q., Zhang, X., Luo, J.: Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design 145(041409) (2023) https://doi.org/10.1115/1.4056598 . Accessed 2023-06-14 Ma et al. [2023] Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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[2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. 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[2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG]
  47. Ma, K., Grandi, D., McComb, C., Goucher-Lambert, K.: Conceptual Design Generation Using Large Language Models (2023) arXiv:2306.01779 [cs.CL] Yuan et al. [2021] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. 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[2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Yuan, C., Marion, T., Moghaddam, M.: Leveraging End-User Data for Enhanced Design Concept Evaluation: A Multimodal Deep Regression Model. Journal of Mechanical Design 144(2) (2021) https://doi.org/10.1115/1.4052366 Song et al. [2023a] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Song, B., Miller, S., Ahmed, F.: Attention-Enhanced Multimodal Learning for Conceptual Design Evaluations. Journal of Mechanical Design 145(4) (2023) https://doi.org/10.1115/1.4056669 Song et al. [2023b] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Song, B., Zhou, R., Ahmed, F.: Multi-modal Machine Learning in Engineering Design: A Review and Future Directions (2023) arXiv:2302.10909 [cs.LG] Li et al. [2023] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. [2023] Regenwetter, L., Srivastava, A., Gutfreund, D., Ahmed, F.: Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design (2023) arXiv:2302.02913 [cs.LG] Li, K., Hopkins, A.K., Bau, D., Viégas, F., Pfister, H., Wattenberg, M.: Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (2023) arXiv:2210.13382 [cs.LG] Belinkov [2022] Belinkov, Y.: Probing classifiers: Promises, shortcomings, and advances. Computational Linguistics 48(1), 207–219 (2022) https://doi.org/10.1162/coli_a_00422 Nye et al. [2021] Nye, M., Andreassen, A.J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., Sutton, C., Odena, A.: Show Your Work: Scratchpads for Intermediate Computation with Language Models (2021) arXiv:2112.00114 [cs.LG] Creswell and Shanahan [2022] Creswell, A., Shanahan, M.: Faithful Reasoning Using Large Language Models (2022) arXiv:2208.14271 [cs.AI] Fricke [1996] Fricke, G.: Successful individual approaches in engineering design. Research in Engineering Design 8(3), 151–165 (1996) https://doi.org/10.1007/BF01608350 Pahl et al. [2007] Pahl, G., Beitz, W., Feldhusen, J., Grote, K.-H.: Engineering Design. Springer, London (2007). https://doi.org/10.1007/978-1-84628-319-2 Regenwetter et al. 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