README.md

---
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
---

<p align="center">
  <img src="assets/logo.png" alt="RealPDEBench logo" width="700" />
</p>

# RealPDEBench

[![HF Dataset](https://img.shields.io/badge/HF%20Dataset-RealPDEBench-FFD21E?logo=huggingface)](https://huggingface.co/datasets/AI4Science-WestlakeU/RealPDEBench)
[![arXiv](https://img.shields.io/badge/arXiv-2601.01829-b31b1b?logo=arxiv)](https://arxiv.org/abs/2601.01829)
[![Website & Docs](https://img.shields.io/badge/Website%20%26%20Docs-realpdebench.github.io-1f6feb?logo=google-chrome)](https://realpdebench.github.io/)
[![Codebase](https://img.shields.io/badge/Codebase-GitHub-181717?logo=github)](https://github.com/AI4Science-WestlakeU/RealPDEBench)
[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-9cf?logo=creativecommons&logoColor=white)](https://creativecommons.org/licenses/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.

- **Website & documentation**: [realpdebench.github.io](https://realpdebench.github.io/)
- **Benchmark codebase**: [AI4Science-WestlakeU/RealPDEBench](https://github.com/AI4Science-WestlakeU/RealPDEBench)

<p align="center">
  <img src="assets/figure1.png" alt="RealPDEBench overview figure" width="900" style="max-width: 100%; height: auto;" />
</p>
<p align="center">
  <em>Figure 1. RealPDEBench provides paired real-world measurements and matched numerical simulations for sim-to-real evaluation.</em>
</p>

## 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** (≈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 | 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.

```python
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:

```python
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:

```json
[
  {"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:

```bibtex
@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`