Automatically Learning Hybrid Digital Twins of Dynamical Systems (2410.23691v1)

Abstract: Digital Twins (DTs) are computational models that simulate the states and temporal dynamics of real-world systems, playing a crucial role in prediction, understanding, and decision-making across diverse domains. However, existing approaches to DTs often struggle to generalize to unseen conditions in data-scarce settings, a crucial requirement for such models. To address these limitations, our work begins by establishing the essential desiderata for effective DTs. Hybrid Digital Twins ($\textbf{HDTwins}$) represent a promising approach to address these requirements, modeling systems using a composition of both mechanistic and neural components. This hybrid architecture simultaneously leverages (partial) domain knowledge and neural network expressiveness to enhance generalization, with its modular design facilitating improved evolvability. While existing hybrid models rely on expert-specified architectures with only parameters optimized on data, $\textit{automatically}$ specifying and optimizing HDTwins remains intractable due to the complex search space and the need for flexible integration of domain priors. To overcome this complexity, we propose an evolutionary algorithm ($\textbf{HDTwinGen}$) that employs LLMs to autonomously propose, evaluate, and optimize HDTwins. Specifically, LLMs iteratively generate novel model specifications, while offline tools are employed to optimize emitted parameters. Correspondingly, proposed models are evaluated and evolved based on targeted feedback, enabling the discovery of increasingly effective hybrid models. Our empirical results reveal that HDTwinGen produces generalizable, sample-efficient, and evolvable models, significantly advancing DTs' efficacy in real-world applications.

• The paper introduces a novel hybrid modeling framework that merges mechanistic models and neural networks for enhanced generalization in Digital Twins.
• It presents HDTwinGen, which uses evolutionary algorithms and LLMs to automate the design and optimization of hybrid digital twin models.
• Empirical tests demonstrate improved out-of-distribution generalization, sample efficiency, and modular evolvability for dynamic system modeling.

Overview of "Automatically Learning Hybrid Digital Twins of Dynamical Systems"

The paper "Automatically Learning Hybrid Digital Twins of Dynamical Systems" introduces a novel approach to enhance the modeling of dynamical systems through the creation of Hybrid Digital Twins (HDTwins). Digital Twins (DTs), which simulate real-world systems, are often limited by their capacity to generalize to unseen conditions, especially in data-scarce environments. This research addresses these challenges by proposing a hybrid architecture that combines mechanistic models with neural network components.

The authors present HDTwins as a potent alternative to traditional DT models, which typically rely either on purely mechanistic approaches or purely neural network-based models. Their work is motivated by the need for DTs that can offer robust out-of-distribution generalization, sample-efficient learning, and the ability to evolve with minimal retraining. To this end, the authors propose an innovative method, HDTwinGen, leveraging evolutionary algorithms and LLMs to automate the design and optimization of HDTwins.

Key Contributions

1. Hybrid Modeling Framework: The paper introduces a hybrid architecture for DTs that combines mechanistic models with neural components. This hybrid approach utilizes domain-grounded priors through mechanistic models and the expressiveness of neural networks to achieve enhanced generalization and regularization, particularly valuable in data-scarce conditions.
2. HDTwinGen for Automated Design: The proposed HDTwinGen framework uses LLMs to automatically specify and optimize hybrid models. This process involves an evolutionary algorithm where LLMs propose model structures, and optimization tools then refine parameters based on empirical data. This method marks a shift from reliance on expert-specified models to automated hybrid model design, paving the way for efficient and scalable DT development.
3. Empirical Validation: Through empirical tests, HDTwinGen was shown to produce DT models with better out-of-distribution generalization, improved sample efficiency, and greater flexibility for modular evolvability. These results underscore the potential of automated hybrid modeling to advance the state-of-the-art in DT applications.

Implications and Future Directions

The implications of this research are significant for the field of AI and modeling of dynamical systems. By automating the creation of DTs, especially in settings where data is limited, HDTwins offer a pathway towards more adaptive and reliable models. This approach may significantly reduce the time and expertise required to develop effective models, fostering broader applicability across various domains such as healthcare, engineering, and environmental sciences.

Looking forward, the deployment of HDTwinGen in real-world scenarios could spur further enhancements in DT frameworks. Future research may explore the integration of additional domain-specific constraints or the incorporation of more sophisticated neural components. Moreover, as LLM capabilities advance, their role in hybrid model optimization is likely to grow, enabling even more intelligent and context-aware model designs.

In conclusion, this paper provides a compelling advancement in the domain of Digital Twins through innovative hybrid modeling, automated design, and optimization using LLMs. It sets a foundation for future research to further refine and adopt these technologies across a spectrum of complex, data-scarce environments.