This repository contains code for the following papers:
-
PROSE: Predicting Multiple Operators and Symbolic Expressions using Multimodal Transformers. More details can be found in
prose_ode/README.md. -
Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation. More details can be found in
prose_pde/README.md. -
PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics. More details can be found in
prose_fd/README.md.
Using conda and the env.yml file:
conda env create --name prose --file=env.yml
If you find our paper and code useful, please consider citing:
@article{liu2024prose,
title={{PROSE}: Predicting multiple operators and symbolic expressions using multimodal transformers},
author={Liu, Yuxuan and Zhang, Zecheng and Schaeffer, Hayden},
journal={Neural Networks},
volume={180},
pages={106707},
year={2024},
publisher={Elsevier}
}
@article{sun2024towards,
title = {Towards a foundation model for partial differential equations: Multioperator learning and extrapolation},
author = {Sun, Jingmin and Liu, Yuxuan and Zhang, Zecheng and Schaeffer, Hayden},
journal = {Phys. Rev. E},
volume = {111},
issue = {3},
pages = {035304},
numpages = {18},
year = {2025},
month = {Mar},
publisher = {American Physical Society},
doi = {10.1103/PhysRevE.111.035304},
url = {https://link.aps.org/doi/10.1103/PhysRevE.111.035304}
}
@article{liu2024prose_fd,
title={{PROSE-FD}: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics},
author={Liu, Yuxuan and Sun, Jingmin and He, Xinjie and Pinney, Griffin and Zhang, Zecheng and Schaeffer, Hayden},
journal={arXiv preprint arXiv:2409.09811},
year={2024}
}