README.md

metadata

license: cc-by-4.0
tags:
  - physics
  - pde
  - neural-operator
  - fluid-dynamics
  - combustion
  - benchmark
datasets:
  - AI4Science-WestlakeU/RealPDEBench

RealPDEBench Model Checkpoints

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

Models (10 architectures)

Model	Type	File Size (per checkpoint)
DPOT-L	Transformer	2.5-2.6 GB
FNO	Spectral	385M-2.1G
Galerkin Transformer	Transformer	386-642M
WDNO	Diffusion	351M-1.4G
DPOT-S	Transformer	118-159M
U-Net	CNN	88-89M
CNO	Hybrid	31M
MWT	Wavelet	22M
Transolver	Transformer	17M
DeepONet	Neural Operator	14M

Scenarios (5)

Scenario	Description
cylinder	Flow past a circular cylinder
controlled_cylinder	Actively controlled cylinder flow
fsi	Fluid-structure interaction
foil	Flow past an airfoil
combustion	Turbulent combustion

Training Paradigms

File	Paradigm
numerical.pth	Trained on numerical simulation data only
real.pth	Trained on real experimental data only
finetune.pth	Pretrained on numerical, finetuned on real
numerical_base_for_finetune.pth	Numerical pretrain base (DPOT-S/L only)

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:

@misc{hu2026realpdebenchbenchmarkcomplexphysical,
      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},
      year={2026},
      eprint={2601.01829},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2601.01829}, 
}

License

CC BY 4.0