Implementation in TensorFlow 2.0 of different examples put together by Raissi et al. on their original publication about Physics Informed Neural Networks.
By designing a custom loss function for standard fully-connected deep neural networks, enforcing the known laws of physics governing the different setups, their work showed that it was possible to either solve or discover with surprisingly good accuracy Partial Differential Equations from noisy and scarce data. The very kind that is widespread in real-life applications.
Don’t forget the --recursive flag while cloning, in order to fetch the experiments data put together by Raissi et al:
git clone https://github.com/pierremtb/PINNs-TF2.0 --recursiveFor more information, please refer to the following: (https://maziarraissi.github.io/PINNs/)
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Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations." arXiv preprint arXiv:1711.10561 (2017).
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Raissi, Maziar, Paris Perdikaris, and George Em Karniadakis. "Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations." arXiv preprint arXiv:1711.10566 (2017).
@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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Copyright (c) 2019 Pierre Jacquier