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 (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) orpip install notebook
Peripheral Dependencies:
- numpy:
pip install numpy - seaborn:
pip install seaborn - matplotlib:
pip install matplotlib - pyDOE (for Tensorflow Implementation):
pip install pyDOE