/PINNs

PyTorch Implementation of Physics-informed Neural Networks

Primary LanguageJupyter Notebook

PINNs (Physics-informed Neural Networks)

This is a simple implementation of the Physics-informed Neural Networks (PINNs) using PyTorch and Tensorflow.

Attribute

Original Work: Maziar Raissi, Paris Perdikaris, and George Em Karniadakis

Github Repo : https://github.com/maziarraissi/PINNs

Link: https://github.com/maziarraissi/PINNs/tree/master/appendix/continuous_time_identification%20(Burgers)

@article{raissi2017physicsI, title={Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations}, author={Raissi, Maziar and Perdikaris, Paris and Karniadakis, George Em}, journal={arXiv preprint arXiv:1711.10561}, year={2017} }

@article{raissi2017physicsII, title={Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations}, author={Raissi, Maziar and Perdikaris, Paris and Karniadakis, George Em}, journal={arXiv preprint arXiv:1711.10566}, year={2017} }

Dependencies

Major Dependencies:

  • Tensorflow (for Tensorflow Implementation): pip install --upgrade tensorflow
  • PyTorch (for PyTorch Implementation): ```pip install --upgrade torch``
  • Jupyter Notebook/Lab: pip install jupyterlab (JupyterLab) or pip install notebook

Peripheral Dependencies:

  • numpy: pip install numpy
  • seaborn: pip install seaborn
  • matplotlib: pip install matplotlib
  • pyDOE (for Tensorflow Implementation): pip install pyDOE