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)
- Developers:
-
Roy, Pratanu [1] ; Castonguay, Stephen [1]
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Release Date:
- 2024-06-20
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Version:
- 0.1
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE National Nuclear Security Administration (NNSA)Primary Award/Contract Number:AC52-07NA27344
- Code ID:
- 143981
- Site Accession Number:
- LLNL-CODE-868717
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Country of Origin:
- United States
Citation Formats
Roy, Pratanu, and Castonguay, Stephen.
PhyML.
Computer Software.
https://github.com/LLNL/PhyML.
USDOE National Nuclear Security Administration (NNSA).
20 Jun. 2024.
Web.
doi:10.11578/dc.20240918.7.
Roy, Pratanu, & Castonguay, Stephen.
(2024, June 20).
PhyML.
[Computer software].
https://github.com/LLNL/PhyML.
https://doi.org/10.11578/dc.20240918.7.
Roy, Pratanu, and Castonguay, Stephen.
"PhyML." Computer software.
June 20, 2024.
https://github.com/LLNL/PhyML.
https://doi.org/10.11578/dc.20240918.7.
@misc{
doecode_143981,
title = {PhyML},
author = {Roy, Pratanu and Castonguay, Stephen},
abstractNote = {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)},
doi = {10.11578/dc.20240918.7},
url = {https://doi.org/10.11578/dc.20240918.7},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20240918.7}},
year = {2024},
month = {jun}
}