README.md

metadata

license: mit
pretty_name: PROSE

This is data for :

1. PROSE-PDE paper: Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation.
2. LeMON: Learning to Learn Multi-Operator Networks.
3. PROSE-SymPy: Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model.

Citation

If you find our paper and code useful, please consider citing:

@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{sun2024lemon,
  title={Lemon: Learning to learn multi-operator networks},
  author={Sun, Jingmin and Zhang, Zecheng and Schaeffer, Hayden},
  journal={arXiv preprint arXiv:2408.16168},
  year={2024}
}

@article{jollie2025time,
    author  = {Derek  Jollie and Jingmin  Sun and Zecheng  Zhang and Hayden Schaeffer},
    title   = {TIME-SERIES FORECASTING AND REFINEMENT WITHIN A MULTIMODAL PDE FOUNDATION MODEL},
    journal = {Journal of Machine Learning for Modeling and Computing},
    issn    = {2689-3967},
    year    = {2025},
    volume  = {6},
    number  = {2},
    pages   = {77--89}
}