https://github.com/adityalab/pinnsformer
https://github.com/adityalab/pinnsformer
Last synced: 28 days ago
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- Host: GitHub
- URL: https://github.com/adityalab/pinnsformer
- Owner: AdityaLab
- Created: 2023-07-14T18:46:23.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-20T23:09:56.000Z (6 months ago)
- Last Synced: 2024-10-21T03:09:42.704Z (6 months ago)
- Language: Python
- Size: 30.4 MB
- Stars: 73
- Watchers: 2
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
## Publication
Implementation of the paper "PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks."
Authors: Leo Zhiyuan Zhao, Xueying Ding, B.Aditya Prakash
Placement: ICLR 2024 Poster
Paper + Appendix: [https://arxiv.org/abs/2307.11833](https://arxiv.org/abs/2307.11833)
## Training
We also provide demo notebooks for convection, 1d_reaction, 1d_wave, and Navier-Stokes PDEs. The demos include all code for training, testing, and ground truth acquirement.
To visualize the loss landscape, run the above command to train and save the model first, then run the script:
```
python3 vis_landscape.py
```
Please adapt the model path accordingly.
## Contact
If you have any questions about the code, please contact Leo Zhiyuan Zhao at ```leozhao1997[at]gatech[dot]edu```.
## Citation
If you find our work useful, please cite our work:
```
@article{zhao2023pinnsformer,
title={PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks},
author={Zhao, Leo Zhiyuan and Ding, Xueying and Prakash, B Aditya},
journal={arXiv preprint arXiv:2307.11833},
year={2023}
}
```