This repository contains code accompanying the paper Towards multi-spatiotemporal-scale generalized PDE modeling, and as such we hope this serves as a starting point for future PDE surrogate learning research. We have imported models from Clifford neural layers for PDE modeling and Geometric Clifford Algebra Networks.
For details about usage please see documentation. If you have any questions or suggestions please open a discussion. If you notice a bug, please open an issue.
If you find this repository useful in your research, please consider citing the following papers:
Initial PDE arena, architecture zoo, Navier-Stokes and Shallow Water datasets:
@article{gupta2022towards,
title={Towards Multi-spatiotemporal-scale Generalized PDE Modeling},
author={Gupta, Jayesh K and Brandstetter, Johannes},
journal={arXiv preprint arXiv:2209.15616},
year={2022}
}3D Clifford FNO layers, Maxwell data:
@article{brandstetter2022clifford,
title={Clifford neural layers for PDE modeling},
author={Brandstetter, Johannes and Berg, Rianne van den and Welling, Max and Gupta, Jayesh K},
journal={arXiv preprint arXiv:2209.04934},
year={2022}
}CGAN layers, CGAN-UNet architectures:
@article{ruhe2023geometric,
title={Geometric clifford algebra networks},
author={Ruhe, David and Gupta, Jayesh K and De Keninck, Steven and Welling, Max and Brandstetter, Johannes},
journal={arXiv preprint arXiv:2302.06594},
year={2023}
}Do remember to cite the original papers as well for individual architectures.
You can further checkout our dedicated repo CliffordLayers.
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