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RealPDEBench

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metadata 
license: cc-by-nc-4.0
pretty_name: RealPDEBench
tags:
  - scientific-ml
  - physics
  - pde
  - sim-to-real
  - fluid-dynamics
  - combustion
  - spatiotemporal
task_categories:
  - time-series-forecasting

RealPDEBench logo

RealPDEBench

HF Dataset arXiv Website & Docs Codebase License: CC BY-NC 4.0

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.

RealPDEBench overview figure

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 (full resolution) Trajectories (real / numerical)
cylinder velocity (u,v) (u,v,p) 3990 128×256 92 / 92
controlled_cylinder (u,v) (u,v,p) (+ control params in filenames) 3990 128×256 96 / 96
fsi (u,v) (u,v,p) 2173 128×128 51 / 51
foil (u,v) (u,v,p) 3990 128×256 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, vo, x, y, t, 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)
    • vo (bytes): float32 array (T_full, H, W) — vorticity
    • x (bytes): float32 array (H, W) — spatial x-coordinate grid (time-invariant)
    • y (bytes): float32 array (H, W) — spatial y-coordinate grid (time-invariant)
    • t (bytes): float32 array (T_full,) — time stamps
    • shape_t (int): complete trajectory length (e.g., 3990, 2173)
    • shape_h, shape_w (int): spatial dimensions

Note on spatial grids: x and y are identical across all time frames, so they are stored once as (H, W) instead of (T, H, W). For methods that require per-frame coordinate grids (e.g., PINNs), broadcast at runtime: x_grid = np.broadcast_to(x[np.newaxis, :, :], (T, H, W)). This is a zero-copy view with no memory overhead.

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)
    • x (bytes): float32 array (H, W) — spatial x-coordinate grid (time-invariant)
    • y (bytes): float32 array (H, W) — spatial y-coordinate grid (time-invariant)
    • t (bytes): float32 array (T_full,) — time stamps
    • 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: ~783GB across all scenarios (full resolution, all fields)
  • Largest shard file: ~2.1GB (well below the Hub's recommended <50GB per file)
  • Total Arrow file count: 649 files (well below the Hub's recommended <100k files per repo)

Per-scenario totals:

Scenario real numerical Total
cylinder 36GB 144GB 180GB
controlled_cylinder 25GB 151GB 176GB
fsi 44GB 58GB 102GB
foil 103GB 155GB 258GB
combustion 4GB 63GB 67GB
Total 212GB 571GB ~783GB

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:

@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}
}

Contact

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