AI4Science-WestlakeU/RealPDEBench-models

RealPDEBench Model Checkpoints

Trained model checkpoints for RealPDEBench, a benchmark for evaluating neural PDE solvers on real-world experimental data.

Updated 2026-09-21: cylinder/cno checkpoints (numerical, real, finetune).

Models (10 architectures)

Scenarios (5)

Training Paradigms

<details> <summary><b>Why DPOT-S/L include <code>numericalbasefor_finetune.pth</code></b></summary>

For DPOT-S/L, numerical.pth and numerical_base_for_finetune.pth are two snapshots from the same numerical pretraining run. numerical.pth is the best-val checkpoint, kept for numerical-only evaluation. numerical_base_for_finetune.pth is the last checkpoint of that same run, used as the starting point for finetuning.

DPOT is a foundation model carrying dataset-level bias from its original pretraining data. The best-val checkpoint of the numerical stage is premature for use as a finetune starting point, and finetuning from a premature checkpoint defeats the purpose of the numerical pretraining stage.

For all other (from-scratch) models, numerical.pth (best-val) serves both as the evaluation checkpoint and as the finetune starting point, so no extra file is needed.

</details>

Directory Structure

{scenario}/{model}/{paradigm}.pth
configs/{scenario}/{model}.yaml

Example: cylinder/fno/finetune.pth + configs/cylinder/fno.yaml

Quick Start

Install

git clone https://github.com/AI4Science-WestlakeU/RealPDEBench.git
cd RealPDEBench && pip install -e .

Download a Single Checkpoint

from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="AI4Science-WestlakeU/RealPDEBench-models",
    filename="cylinder/fno/finetune.pth",
)

Download All Checkpoints for a Scenario

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="AI4Science-WestlakeU/RealPDEBench-models",
    allow_patterns="cylinder/**",
    local_dir="./checkpoints",
)

Evaluate

python eval.py --config configs/cylinder/fno.yaml \
    --checkpoint_path ./checkpoints/cylinder/fno/finetune.pth \
    --dataset_type real --test_mode all

Checkpoint Format

checkpoint = torch.load("cylinder/fno/finetune.pth")
# Keys: model_state_dict, train_losses, val_losses,
#        iteration, best_iteration, best_val_loss

DPOT Pretrained Weights

DPOT models require pretrained backbone weights (not included here). Download via:

# Option 1: Built-in download script
python -m realpdebench.utils.dpot_ckpts_dl

# Option 2: From HuggingFace directly
# https://huggingface.co/hzk17/DPOT

Dataset

The corresponding dataset is hosted at: AI4Science-WestlakeU/RealPDEBench

Citation

If you find our work and/or our code useful, please cite us via:

@inproceedings{hu2026realpdebench,
      title={RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data}, 
      author={Peiyan Hu and Haodong Feng and Hongyuan Liu and Tongtong Yan and Wenhao Deng and Tianrun Gao and Rong Zheng and Haoren Zheng and Chenglei Yu and Chuanrui Wang and Kaiwen Li and Zhi-Ming Ma and Dezhi Zhou and Xingcai Lu and Dixia Fan and Tailin Wu},
      booktitle={The Fourteenth International Conference on Learning Representations},
      year={2026},
      url={https://openreview.net/forum?id=y3oHMcoItR},
      note={Oral Presentation}
}

License

CC BY 4.0