ocibaba / graphGalerkin

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

graphGalerkin

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems

requirement

Please add pyCaMOtk folder to your python path, so your system can find it, such as export PYTHONPATH=$PYTHONPATH:your_path/pycamotk

Citation

If you find this repo useful for your research, please consider to cite:

@article{gao2022physics,
  title={Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems},
  author={Gao, Han and Zahr, Matthew J and Wang, Jian-Xun},
  journal={Computer Methods in Applied Mechanics and Engineering},
  volume={390},
  pages={114502},
  year={2022},
  publisher={Elsevier},
  doi={https://doi.org/10.1016/j.cma.2021.114502}
}