RealPDEBench: A Benchmark for Complex Physical Systems with Paired Real-World and Simulated Data

Peiyan Hu^∗†1,3, Haodong Feng^*1, Hongyuan Liu^*1, Tongtong Yan², Wenhao Deng¹, Tianrun Gao^†1,4, Rong Zheng^†1,5, Haoren Zheng^†1,2, Chenglei Yu¹, Chuanrui Wang¹, Kaiwen Li^†1,2, Zhi-Ming Ma³, Dezhi Zhou², Xingcai Lu⁶, Dixia Fan¹, Tailin Wu^†1.

¹School of Engineering, Westlake University; ²Global College, Shanghai Jiao Tong University; ³Academy of Mathematics and Systems Science, Chinese Academy of Sciences; ⁴Department of Geotechnical Engineering, Tongji University; ⁵School of Physics, Peking University; ⁶Key Laboratory for Power Machinery and Engineering of M. O. E., Shanghai Jiao Tong University

*Equal contribution, †Work done as an intern at Westlake University, †Corresponding authors

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💧🔥 Overview

RealPDEBench is the first scientific ML benchmark with paired real-world measurements and matched numerical simulations for complex physical systems, designed for spatiotemporal forecasting and sim-to-real transfer.

At a glance 👀

• 5 Datasets: cylinder, fsi, controlled_cylinder, foil, combustion
• 700+ Trajectories
• 10 Baseline models: U-Net, FNO, CNO, WDNO, DeepONet, MWT, GK-Transformer, Transolver, DPOT, DMD
• 9 Evaluation metrics: RMSE, MAE, Rel L₂, R², Update Ratio, fRMSE, FE, KE, MVPE

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🎬 Installation (pip)

This repo is packaged with pyproject.toml and can be installed via pip (requires Python ≥ 3.10):

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

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⏬ Dataset download

Hugging Face dataset:

The repo id AI4Science-WestlakeU/RealPDEBench.

We provide a small pattern-based downloader:

# safe default: download metadata JSONs only
realpdebench download --dataset-root /path/to/data --scenario cylinder --what metadata

# to download Arrow shards (LARGE), explicitly set --what=hf_dataset or --what=all
# splits are stored in index JSONs under hf_dataset/ (no split directories)
realpdebench download --dataset-root /path/to/data --scenario cylinder --what hf_dataset --dataset-type real

Tips:

• Set --endpoint https://hf-mirror.com (or env HF_ENDPOINT) to get acesss.
• If you hit rate limits (HTTP 429) or need auth, login and set env HF_TOKEN=....
• We recommend setting env HF_HUB_DISABLE_XET=1.

HDF5-format dataset

Coming soon!

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📝 Checkpoint & log file download

Coming soon!

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📥 Training

# Simulated training (train on numerical data)
python -m realpdebench.train --config configs/cylinder/fno.yaml --train_data_type numerical

# Real-world training (train on real data from scratch)
python -m realpdebench.train --config configs/cylinder/fno.yaml --train_data_type real

# Real-world finetuning (finetune on real data)
python -m realpdebench.train --config configs/cylinder/fno.yaml --train_data_type real --is_finetune

Using HF Arrow-backed datasets

HF Arrow datasets are stored under {dataset_root}/{scenario}/hf_dataset/{real,numerical}/ with split index files {split}_index_{type}.json. To use them, enable:

• --use_hf_dataset: load Arrow trajectories + index files (lazy slicing, dynamic N_autoregressive)
• --hf_auto_download: download missing artifacts from HF automatically (use --hf_endpoint https://hf-mirror.com for easy accessing)

Example:

python -m realpdebench.train --config configs/cylinder/fno.yaml --use_hf_dataset --hf_auto_download --hf_endpoint https://hf-mirror.com

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📤 Inference

python -m realpdebench.eval --config configs/cylinder/fno.yaml --checkpoint_path /path/to/checkpoint.pth

Using HF Arrow-backed datasets

python -m realpdebench.eval --config configs/cylinder/fno.yaml --checkpoint_path /path/to/checkpoint.pth --use_hf_dataset

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Numerical scripts

Coming soon!

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👩‍💻 Contribute

We welcome contributions from the community! Please feel free to

• Add your models
• Contact us to submit to the leaderboard
• Contribute code improvements
• Improve documentation

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🫡 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}, 
}

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📚 Related Resources

• AI for Scientific Simulation and Discovery Lab: https://github.com/AI4Science-WestlakeU
• REALM: https://github.com/deepflame-ai/REALM/tree/main
• PDEBench: https://github.com/pdebench/PDEBench