Original PyTorch implementation of UPS proposed in the paper "UPS: Towards Building Foundation Models for PDE Solving via Cross-Modal Adaptation". UPS is developed for solving diverse spatiotemporal PDEs defined over various domains, dimensions, and resolutions. It unifies different PDEs into a consistent representation space and processes diverse collections of PDE data using a unified network architecture that combines LLMs with domain-specific neural operators.

pip install -r requirements.txt

Note that the attrdict package might not be compatible for python 3.10 or newer versions. If getting ImportError: cannot import name 'Mapping' from 'collections', change

from collections import Mapping

to

from collections.abc import Mapping

1. Download PDEBench datasets to ./datasets
2. Generate the PDE metadata

python3 generate_text_embeddings.py

3. Generate the data files for data loading

python3 generate_data.py

4. Use an existing configuration file or add a new one to ./configs
5. Run training

python3 main.py --config configs/config_file_name.yaml 

Model checkpoints will be released later.