RealPDEBench

RealPDEBench is a benchmark of paired real-world measurements and matched numerical simulations for complex physical systems. It is designed for spatiotemporal forecasting and sim-to-real transfer evaluation on real data.

This Hub repository (AI4Science-WestlakeU/RealPDEBench) is the release repo for RealPDEBench.

• Website & documentation: realpdebench.github.io
• Benchmark codebase: AI4Science-WestlakeU/RealPDEBench

Figure 1. RealPDEBench provides paired real-world measurements and matched numerical simulations for sim-to-real evaluation.

What makes RealPDEBench different?

• Paired real + simulated data: each scenario provides experimental measurements and corresponding CFD/LES simulations.
• Real-world evaluation: models are evaluated on real trajectories to quantify the sim-to-real gap.
• Multi-modal mismatch: simulations include additional unmeasured modalities (e.g., pressure, species fields), enabling modality-masking and transfer strategies.

Data sources (high level)

• Fluid systems (cylinder, controlled_cylinder, fsi, foil):
  • Real: Particle Image Velocimetry (PIV) in a circulating water tunnel
  • Sim: CFD (2D finite-volume + immersed-boundary; 3D GPU solvers depending on scenario)
• Combustion (combustion):
  • Real: OH* chemiluminescence imaging (high-speed)
  • Sim: Large Eddy Simulation (LES) with detailed chemistry (NH3/CH4/air co-firing)

Scenarios (5)

Scenario	Real data (measured)	Numerical data (simulated)	Frames / trajectory	Spatial grid (after sub-sampling)	HDF5 trajectories (real / numerical)
cylinder	velocity (u,v)	(u,v,p)	3990	64×128	92 / 92
controlled_cylinder	(u,v)	(u,v,p) (+ control params in filenames)	3990	64×128	96 / 96
fsi	(u,v)	(u,v,p)	2173	64×64	51 / 51
foil	(u,v)	(u,v,p)	3990	64×128	98 / 99
combustion	OH* chemiluminescence intensity (1 channel)	intensity surrogate (1) + 15 simulated fields	2001	128×128	30 / 30

Total trajectories (HDF5 files): ~735 (≈364 real + ≈368 numerical).

Physical parameter ranges (real experiments)

Scenario	Key parameters (real)
cylinder	Reynolds number (Re): 1800–12000
controlled_cylinder	(Re): 1781–9843; control frequency (f): 0.5–1.4 Hz
fsi	(Re): 3272–9068; mass ratio (m^*): 18.2–20.8
foil	angle of attack (\alpha): 0°–20°; (Re): 2968–17031
combustion	CH4 ratio: 20–100%; equivalence ratio (\phi): 0.75–1.3

Data format on the Hub

RealPDEBench stores complete trajectories in HuggingFace Arrow format, with separate JSON index files for train/val/test splits. This enables dynamic N_autoregressive support at runtime.

Each scenario contains:

• Trajectory data: hf_dataset/{real,numerical}/ — Arrow files with complete time series
• Index files: hf_dataset/{split}_index_{type}.json — maps sample indices to (sim_id, time_id)
• test_mode metadata: {in_dist,out_dist,remain}_params_{type}.json

Repository layout:

{repo_root}/
  cylinder/
    in_dist_test_params_real.json
    out_dist_test_params_real.json
    remain_params_real.json
    in_dist_test_params_numerical.json
    out_dist_test_params_numerical.json
    remain_params_numerical.json
    hf_dataset/
      real/                           # Arrow: complete trajectories (92 files)
        data-*.arrow
        dataset_info.json
        state.json
      numerical/                      # Arrow: complete trajectories
        data-*.arrow
        dataset_info.json
        state.json
      train_index_real.json           # Index: [{"sim_id": "xxx.h5", "time_id": 0}, ...]
      val_index_real.json
      test_index_real.json
      train_index_numerical.json
      val_index_numerical.json
      test_index_numerical.json
  fsi/
    ...  (same structure)
  controlled_cylinder/
    ...  (same structure)
  foil/
    ...  (same structure)
  combustion/
    ...  (same structure)

How to download only what you need

For large data, use snapshot_download(..., allow_patterns=...) to avoid pulling the full repository.

import os
from huggingface_hub import snapshot_download
from datasets import load_from_disk

repo_id = "AI4Science-WestlakeU/RealPDEBench"
os.environ["HF_HUB_DISABLE_XET"] = "1"
local_dir = snapshot_download(
    repo_id=repo_id,
    repo_type="dataset",
    allow_patterns=["fsi/**"],  # example: download only the FSI folder
    endpoint="https://hf-mirror.com",
)

# Load trajectory data
trajectories = load_from_disk(os.path.join(local_dir, "fsi", "hf_dataset", "real"))
print(f"Loaded {len(trajectories)} trajectories")
print(trajectories[0].keys())  # sim_id, u, v, shape_t, shape_h, shape_w

Using the RealPDEBench loaders (recommended)

For automatic train/val/test splitting and dynamic N_autoregressive support, use the provided dataset loaders:

from realpdebench.data.fluid_hf_dataset import FSIHFDataset

dataset = FSIHFDataset(
    dataset_name="fsi",
    dataset_root="/path/to/data",
    dataset_type="real",
    mode="test",
    N_autoregressive=10,  # Dynamic! Works with any value
)

input_tensor, output_tensor = dataset[0]
print(f"Input shape: {input_tensor.shape}")   # (20, H, W, 2)
print(f"Output shape: {output_tensor.shape}") # (200, H, W, 2) = 20 × 10

Schema (columns)

Fluid datasets (cylinder, controlled_cylinder, fsi, foil)

• Keys (each row = one complete trajectory):
  • sim_id (string): trajectory file name (e.g., 10031.h5)
  • u, v (bytes): float32 arrays of shape (T_full, H, W) — complete time series
  • p (bytes): float32 array (T_full, H, W) (numerical splits only)
  • shape_t (int): complete trajectory length (e.g., 3990, 2173)
  • shape_h, shape_w (int): spatial dimensions

Combustion dataset (combustion)

• Keys (each row = one complete trajectory):
  • sim_id (string): e.g., 40NH3_1.1.h5
  • observed (bytes): float32 array (T_full, H, W) — complete time series
  • numerical (bytes): float32 array (T_full, H, W, 15) (numerical splits only)
  • numerical_channels (int): number of numerical channels (15)
  • shape_t (int): complete trajectory length (e.g., 2001)
  • shape_h, shape_w (int): spatial dimensions

Index files (JSON)

Each split has an index file mapping sample indices to trajectory positions:

[
  {"sim_id": "10031.h5", "time_id": 0},
  {"sim_id": "10031.h5", "time_id": 20},
  {"sim_id": "10031.h5", "time_id": 40},
  ...
]

Data size

• Total: ~210GB across all scenarios
• Largest shard file: ~0.5GB (well below the Hub's recommended <50GB per file)
• Total file count: ~550 files (well below the Hub's recommended <100k files per repo)

Per-scenario totals:

Scenario	real	numerical	Total
cylinder	23GB	34GB	57GB
controlled_cylinder	24GB	36GB	59GB
fsi	6GB	11GB	17GB
foil	24GB	37GB	61GB
combustion	1GB	15GB	16GB
Total	78GB	133GB	~210GB

Recommended benchmark protocols

RealPDEBench supports three standard training paradigms (all evaluated on real-world data):

• Simulated training (numerical only)
• Real-world training (real only)
• Simulated pretraining + real finetuning

License

This dataset is released under CC BY‑NC 4.0 (non‑commercial). Please credit the authors and the benchmark paper when using the dataset.

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}, 
}

Contact

AI for Scientific Simulation and Discovery Lab, Westlake University
Maintainer: westlake-ai4s (Hugging Face)
Org: AI4Science-WestlakeU