PROSE-PDE V1.0

bash scripts/gen_data.sh

By default, the generated data is saved in the Dir/type_name/ directory with the following structure:

To generate specific datasets, modify the file_name variable in gen_data.sh. Examples are provided within the script.

When using a custom file_name, the output structure becomes:

For test runs:

bash scripts/run.sh

For the experiments in the paper Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model:

bash scripts/sympy.sh

For the experiments in the paper Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation:

bash scripts/pde_experiments.sh

Just specify your data.train_types and data.eval_types and if some specific_name needed, add data.eval_data=specific_name and data.train_data=specific_name

Num of training and Num of evaluation:

data.train_size is the num of training sample in total, and we subsample data.train_size_get for real training. Same for data.eval_size and data.eval_size_get

We use skip = data.train_size (~line 54 of evaluator.py) to avoid sampling the same data for training and evaluation. However, if you want to save space/ your evaluation dataset and training dataset are different, you can comment that out.

Note you may set model.data_decoder.full_tx=false to run with a larger batch_size

In data configuration, you can enable/disable the skeleton tree input by symbol.use_skeleton=True/False

In model configuration, you can include/exclude the text (symbol) encoder/decoder by model.no_text_encoder=True/False and model.no_text_decoder=True/False , the default setting is text encoder but no text decoder.

If you find this code useful, please consider citing:

@article{sun2025towards,
  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{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}
}