This repository explores Physics-Informed Neural Networks (PINNs) using PyTorch.
PINNs integrate physical laws, expressed as partial differential equations (PDEs), into the training of neural networks.
This approach is especially useful when data is scarce but the governing physics is well-understood.
This project demonstrates how to implement PINNs to solve PDEs by embedding physical constraints directly into the loss function of a neural network.
The learned solutions not only fit the data but also respect the underlying physical laws.
📝 For a detailed tutorial, check out the accompanying Medium article.
pinns/
├── src/ # Core implementation: models, training loops, etc.
├── LICENSE
└── README.md # You're here!
Install the required packages using pip:
pip install torch scikit-learn numpy matplotlib seaborn
git clone https://github.com/TheodoreWolf/pinns.git
cd pinns
To get started, open one of the Jupyter notebooks in the src/ directory:
jupyter notebook src/
These walk through training PINNs on various PDEs with visualizations.
Alternatively, run Python scripts in src/ to train directly via the command line.
- 📘 Medium Tutorial: Physics-Informed Neural Networks: A Simple Tutorial with PyTorch
- 📄 Original Paper: Raissi, Perdikaris, & Karniadakis (2019)
Physics-informed neural networks (PINNs)
This project is licensed under the MIT License. See the LICENSE file for details.
Inspired by the foundational work by Raissi et al.
This repo aims to provide an approachable and practical introduction to PINNs with PyTorch.