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. The full dataset upload is planned alongside the paper posting (mid‑Jan 2026, arXiv). If you are reading this before the release, you may only see a small sample and metadata while the upload is in progress.

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 (NH(_3)/CH(_4)/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 (≈367 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	CH(_4) ratio: 20–100%; equivalence ratio (\phi): 0.75–1.3

Data format on the Hub (HF Arrow via `save_to_disk`)

Each split is stored as a Hugging Face datasets.Dataset saved with Dataset.save_to_disk(). Concretely, each split is a directory containing:

data-*.arrow (sharded Arrow files, float32 payloads stored as bytes)
dataset_info.json
state.json

test_mode metadata (JSON)

RealPDEBench supports test_mode evaluation splits (in_dist, out_dist, seen, unseen). The group definitions are shipped as JSON dicts per scenario:

in_dist_test_params_{type}.json
out_dist_test_params_{type}.json
remain_params_{type}.json

where {type} is real or numerical.

Temporal windowing (what an “example” means)

RealPDEBench is stored as sliding windows cut from longer trajectories. Each row corresponds to (sim_id, time_id):

sim_id: which trajectory (HDF5 file)
time_id: start index of the window

Typical window lengths (T):

40 frames for cylinder, fsi, foil, combustion (often used as 20‑step input + 20‑step output)
20 frames for controlled_cylinder (often 10 + 10)
20 frames for combustion/surrogate_train (surrogate model training data)

Intended layout for the full release (mirrors the on-disk structure used by RealPDEBench loaders):

{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_train/  real_val/  real_test/
      numerical_train/  numerical_val/  numerical_test/
  fsi/
    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/
      ...
  combustion/
    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_train/  real_val/  real_test/
      numerical_train/              # (val/test intentionally empty)
      surrogate_train/              # combustion-only (surrogate model training)
      surrogate_train_sim_ids.txt
      surrogate_train_meta.json
  ...

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"
# Tip (recommended behind GFW / when using hf-mirror): disable Xet to avoid extra domains.
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",
)

ds = load_from_disk(os.path.join(local_dir, "fsi", "hf_dataset", "numerical_val"))
row = ds[0]
print(row.keys())

Schema (columns)

Fluid datasets (`cylinder`, `controlled_cylinder`, `fsi`, `foil`)

Keys:
- sim_id (string): trajectory file name (e.g., 10031.h5)
- time_id (int): start frame index of the window
- u, v (bytes): float32 arrays of shape (T, H, W)
- p (bytes): float32 array (T, H, W) (numerical splits only)
- shape_t, shape_h, shape_w (int): shapes for decoding

Combustion dataset (`combustion`)

Keys:
- sim_id (string): e.g., 40NH3_1.1.h5
- time_id (int): start frame index of the window
- observed (bytes): float32 array (T, H, W) (real: measured intensity; numerical: surrogate intensity)
- numerical (bytes): float32 array (T, H, W, 15) (numerical splits only)
- numerical_channels (int): number of numerical channels (15)
- shape_t, shape_h, shape_w (int): shapes for decoding

Combustion surrogate-train (`combustion/surrogate_train`)

Used to train a surrogate model mapping simulated modalities → real modality (combustion only).

Keys:
- real (bytes): float32 array (T, H, W) (target intensity)
- numerical (bytes): float32 array (T, H, W, C) (input fields)
- plus shapes (*_shape_*) and numerical_channels

Decoding example

import numpy as np

u = np.frombuffer(row["u"], dtype=np.float32).reshape(
    row["shape_t"], row["shape_h"], row["shape_w"]
)

Current converted data size (local conversion; full release target)

These numbers refer to our current HF Arrow conversion outputs (not all uploaded to this test repo yet):

Total: ~954GB across all scenarios
Largest shard file: ~0.47GB (well below the Hub’s recommended <50GB per file)
Total file count: ~2.1k files (well below the Hub’s recommended <100k files per repo)

Per-scenario totals (HF Arrow):

Scenario	Total size
combustion	622GB
cylinder	116GB
fsi	34GB
controlled_cylinder	61GB
foil	124GB

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 (paper release mid‑Jan 2026)

ArXiv link and BibTeX will be added here at release time.

Contact

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

Downloads last month: 6

RealPDEBench