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PyTorch Implementation of Physics-informed Neural Networks
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| 2026-08-02 | 720 |
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| 2026-08-06 | 720 |
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# 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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## 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}
}
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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```
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ae9655a33cd6baee, llm:Repository description and README: 'Physics-informed Neural Networks', topics: 'pinns, pytorch-implementation', PyTorch and TensorFlow implementations for solving/discovering PDEs (Raissi et al. work).
matched fp:ae9655a33cd6baee, llm:Repository description and README: 'Physics-informed Neural Networks', topics: 'pinns, pytorch-implementation', PyTorch and TensorFlow implementations for solving/discovering PDEs (Raissi et al. work).