PINNs (Physics-informed Neural Networks)

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

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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} }

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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