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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
PINNs-Torch, Physics-informed Neural Networks (PINNs) implemented in PyTorch.
| Date | Stars |
|---|---|
| 2026-07-31 | 937 |
| 2026-08-06 | 937 |
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<div align="center"> <img src="http://drive.google.com/uc?export=view&id=1Sqz8yYnij-7Vjl-4laOxBBCdhE0eDCDe" width="400"> </br> </br> <a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> <a href="https://pytorchlightning.ai/"><img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a> <a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/Config-Hydra-89b8cd"></a> <a href="https://github.com/ashleve/lightning-hydra-template"><img alt="Template" src="https://img.shields.io/badge/-Lightning--Hydra--Template-017F2F?style=flat&logo=github&labelColor=gray"></a> [](https://github.com/psf/black) [](https://colab.research.google.com/github/rezaakb/pinns-torch/blob/main/tutorials/0-Schrodinger.ipynb) <a href="https://openreview.net/forum?id=nl1ZzdHpab">[Paper]</a> - <a href="https://github.com/rezaakb/pinns-tf2">[TensorFlow v2]</a> - <a href="https://github.com/rezaakb/pinns-jax">[JAX]</a> - <a href="https://github.com/maziarraissi/PINNs">[TensorFlow v1]</a> </div> ## Description Our package introduces Physics-Informed Neural Networks (PINNs) implemented using PyTorch. The standout feature is the incorporation of CUDA Graphs and JIT Compilers (TorchScript) for compiling models, resulting in significant performance gains up to 9x compared to the original TensorFlow v1 implementation. <div align="center"> <img src="http://drive.google.com/uc?export=view&id=1WVZSSQwFAyNAkSqNgvZqok2vkPhpoERy" width="1000"> </br> <em>Each subplot corresponds to a problem, with its iteration count displayed at the top. The logarithmic x-axis shows the speed-up factor w.r.t the original code in TensorFlow v1, and the y-axis illustrates the mean relative error.</em> </div> </br> For more information, please refer to our paper: <a href="https://openreview.net/forum?id=nl1ZzdHpab">PINNs-Torch: Enhancing Speed and Usability of Physics-Informed Neural Networks with PyTorch.</a> Reza Akbarian Bafghi, and Maziar Raissi. DLDE III, NeurIPS, 2023. ## Installation PINNs-Torch requires following dependencies to be installed: - [PyTorch](https://pytorch.org) >=2.0.0 - [PyTorch Lightning](https://lightning.ai/) >= 2.0.0 - [Hydra](https://hydra.cc/docs/intro/) >= 1.3 Then, you can install PINNs-Torch itself via \[pip\]: ```bash pip install pinnstorch ``` If you intend to introduce new functionalities or make code modifications, we suggest duplicating the repository and setting up a local installation: ```bash git clone https://github.com/rezaakb/pinns-torch cd pinns-torch # [OPTIONAL] create conda environment conda create -n myenv python=3.9 conda activate myenv # install package pip install -e . ``` ## Quick start Explore a variety of implemented examples within the [examples](examples) folder. To run a specific code, such as the one for the Navier-Stokes PDE, you can use: ```bash python examples/navier_stokes/train.py ``` You can train the model using a specified configuration, like the one found in [examples/navier_stokes/configs/config.yaml](examples/navier_stokes/configs/config.yaml). Parameters can be overridden directly from the command line. For instance: ```bash python examples/navier_stokes/train.py trainer.max_epochs=20 n_train=3000 ``` To utilize our package, there are two primary options: - Implement your training structures using Hydra, as illustrated in our provided examples. - Directly incorporate our package to solve your custom problem. For a practical guide on directly using our package to solve the Schrödinger PDE in a continuous forward problem, refer to our tutorial here: [tutorials/0-Schrodinger.ipynb](tutorials/0-Schrodinger.ipynb). ## Data The data located on the server and will be dow
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