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DOE OSTI · code-143981

PhyML

Abstract

Python scripts for physics informed machine learning for time-dependent problems. The code implements hard constrained sequential physics-informed neural networks (HCSPINNs) using JAX library. The details of the method and implementation can be found in the following paper: Roy, P., & Castonguay, S. (2024). Exact Enforcement of Temporal Continuity in Sequential Physics- Informed Neural Networks. arXiv preprint arXiv:2403.03223. (https://arxiv.org/abs/2403.03223)

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BibTeXRIS

Roy, Pratanu, Castonguay, Stephen. 2024-06-20. PhyML. https://doi.org/10.11578/dc.20240918.7

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