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Differential Informed Auto-Encoder
Published 24 Oct 2024 in cs.LG | (2410.18593v1)
Abstract: In this article, an encoder was trained to obtain the inner structure of the original data by obtain a differential equations. A decoder was trained to resample the original data domain, to generate new data that obey the differential structure of the original data using the physics-informed neural network.
- Zhang Jinrui. desinetask. https://github.com/unjerry/autoCluster/blob/master/deSineTask.bat. Accessed: 2024-10-21.
- Zhang Jinrui. Envreqs. https://github.com/unjerry/autoCluster/blob/master/reqs.bat. Accessed: 2024-10-21.
- Zhang Jinrui. Linear. https://github.com/unjerry/autoCluster/blob/cbf0039/deLinearTasksSine.bat. Accessed: 2024-10-21.
- Zhang Jinrui. Linearaug. https://github.com/unjerry/autoCluster/blob/dabc8c85/deLinearAugTasksCircle.bat. Accessed: 2024-10-23.
- Physics informed deep learning (part i): Data-driven solutions of nonlinear partial differential equations. arXiv preprint arXiv:1711.10561, 2017.
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