@@ -142,9 +143,7 @@ with a 128x128 discretization is created in less than a second on a modern GPU. [3] Montanelli, Hadrien, and Niall Bootland. "Solving periodic semilinear stiff PDEs in 1D, 2D and 3D with exponential integrators." Mathematics and Computers in Simulation 178 (2020): 307-327. -## Acknowledgements - -### Related & Motivation +## Related & Motivation This package is greatly inspired by the [chebfun](https://www.chebfun.org/) library in *MATLAB*, in particular the @@ -178,15 +177,32 @@ This package also took much inspiration from the *Julia* ecosystem, especially for checking the implementation of the contour integral method of [2] and how to handle (de)aliasing. -### Citation +## Citation + +This package was developed as part of the [APEBench paper +(arxiv.org/abs/2411.00180)](https://arxiv.org/abs/2411.00180) (accepted at +Neurips 2024). If you find it useful for your research, please consider citing +it: + +```bibtex +@article{koehler2024apebench, + title={{APEBench}: A Benchmark for Autoregressive Neural Emulators of {PDE}s}, + author={Felix Koehler and Simon Niedermayr and R{\"}udiger Westermann and Nils Thuerey}, + journal={Advances in Neural Information Processing Systems (NeurIPS)}, + volume={38}, + year={2024} +} +``` + +(Feel free to also give the project a star on GitHub if you like it.) -This package was developed as part of the `APEBench paper` (accepted at Neurips 2024), we will soon add the citation here. +[Here](https://github.com/tum-pbs/apebench) you can find the APEBench benchmark suite. -### Funding +## Funding The main author (Felix Koehler) is a PhD student in the group of [Prof. Thuerey at TUM](https://ge.in.tum.de/) and his research is funded by the [Munich Center for Machine Learning](https://mcml.ai/). -### License +## License MIT, see [here](https://github.com/Ceyron/exponax/blob/main/LICENSE.txt) diff --git a/docs/index.md b/docs/index.md index f8c1b57..1d4f391 100644 --- a/docs/index.md +++ b/docs/index.md @@ -73,6 +73,26 @@ that explains the basics of `Exponax`. 7. Normalized interface for reduced number of parameters to uniquely define any dynamics. +## Citation + +This package was developed as part of the [APEBench paper +(arxiv.org/abs/2411.00180)](https://arxiv.org/abs/2411.00180) (accepted at +Neurips 2024). If you find it useful for your research, please consider citing +it: + +```bibtex +@article{koehler2024apebench, + title={{APEBench}: A Benchmark for Autoregressive Neural Emulators of {PDE}s}, + author={Felix Koehler and Simon Niedermayr and R{\"}udiger Westermann and Nils Thuerey}, + journal={Advances in Neural Information Processing Systems (NeurIPS)}, + volume={38}, + year={2024} +} +``` + +(Feel free to also give the project a star on GitHub if you like it.) + +[Here](https://github.com/tum-pbs/apebench) you can find the APEBench benchmark suite. ## License