Physics-Informed-Neural-Networks
This repository provides a PyTorch implementation of the physics informed neural networks by M.Raissi et al.
MIT License
This repository provides a PyTorch implementation of the physics informed neural networks by M.Raissi et al. The following exploration was performed to understand the data used to solve the Burgers' equation. The following plot shows the solution u(t,x) and the prediction.
- Setup the environment
conda create --name PINN python=3.7.9
conda activate PINN
pip install requirements.txt
- Run the experiment
cd src
python main.py --config_path ./config.json
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Raissi, Maziar, Paris Perdikaris, and George E. Karniadakis. "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations." Journal of Computational Physics 378 (2019): 686-707.
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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).