PROSE(1DPDE) version 1.0

Data Generation: See detailed instruction in script/gen_data.sh

bash scripts/gen_data.sh

The data is by default saving to "your_directory/type_name/(type_name)(IC_per_params).prefix" for symbol part and "your_directory/type_name/(type_name)(IC_per_params)_data.h5" for data part

If some specific data need to be generated, change "file_name=specific_name", samples are given in script/gen_data.sh and data will be stored in "your_directory/type_name/(type_name)(IC_per_params)(file_name).prefix" and "your_directory/type_name/(type_name)(IC_per_params)(file_name)_data.h5"

Run the code

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

Data

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

Modes

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.

Citation

If you find this code useful, please consider citing:

@article{sun2024foundation,
      title={Towards a Foundation Model for Partial Differential Equation: Multi-Operator Learning and Extrapolation}, 
      author={Jingmin Sun and Yuxuan Liu and Zecheng Zhang and Hayden Schaeffer},
      year={2024},
      eprint={2404.12355},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}


@article{jollie2024time,
      title={Time-Series Forecasting, Knowledge Distillation, and Refinement within a Multimodal PDE Foundation Model}, 
      author={Derek Jollie and Jingmin Sun and Zecheng Zhang and Hayden Schaeffer},
      year={2024},
      eprint={2409.11609},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}