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--- |
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license: cc-by-nc-4.0 |
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pretty_name: RealPDEBench |
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tags: |
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- scientific-ml |
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- physics |
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- pde |
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- sim-to-real |
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- fluid-dynamics |
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- combustion |
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- spatiotemporal |
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task_categories: |
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- time-series-forecasting |
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--- |
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<p align="center"> |
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<img src="assets/logo.png" alt="RealPDEBench logo" width="700" /> |
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</p> |
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# RealPDEBench |
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> **⚠️ Data Update Notice (2026-01-13)** |
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> |
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> We are updating the dataset format to support dynamic `N_autoregressive` values. The new V2 format will be available before **January 15, 2026**. Please wait for the update before downloading. |
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[](https://huggingface.co/datasets/AI4Science-WestlakeU/RealPDEBench) |
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[](https://arxiv.org/abs/2601.01829) |
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[](https://realpdebench.github.io/) |
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[](https://github.com/AI4Science-WestlakeU/RealPDEBench) |
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[](https://creativecommons.org/licenses/by-nc/4.0/) |
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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*. |
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This Hub repository (`AI4Science-WestlakeU/RealPDEBench`) is the **release repo** for RealPDEBench. |
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- **Website & documentation**: [realpdebench.github.io](https://realpdebench.github.io/) |
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- **Benchmark codebase**: [AI4Science-WestlakeU/RealPDEBench](https://github.com/AI4Science-WestlakeU/RealPDEBench) |
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<p align="center"> |
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<img src="assets/figure1.png" alt="RealPDEBench overview figure" width="900" style="max-width: 100%; height: auto;" /> |
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</p> |
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<p align="center"> |
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<em>Figure 1. RealPDEBench provides paired real-world measurements and matched numerical simulations for sim-to-real evaluation.</em> |
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</p> |
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## What makes RealPDEBench different? |
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- **Paired real + simulated data**: each scenario provides experimental measurements and corresponding CFD/LES simulations. |
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- **Real-world evaluation**: models are evaluated on real trajectories to quantify the sim-to-real gap. |
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- **Multi-modal mismatch**: simulations include additional unmeasured modalities (e.g., pressure, species fields), enabling modality-masking and transfer strategies. |
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## Data sources (high level) |
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- **Fluid systems** (`cylinder`, `controlled_cylinder`, `fsi`, `foil`): |
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- **Real**: Particle Image Velocimetry (PIV) in a circulating water tunnel |
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- **Sim**: CFD (2D finite-volume + immersed-boundary; 3D GPU solvers depending on scenario) |
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- **Combustion** (`combustion`): |
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- **Real**: OH* chemiluminescence imaging (high-speed) |
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- **Sim**: Large Eddy Simulation (LES) with detailed chemistry (NH3/CH4/air co-firing) |
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## Scenarios (5) |
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| Scenario | Real data (measured) | Numerical data (simulated) | Frames / trajectory | Spatial grid (after sub-sampling) | HDF5 trajectories (real / numerical) | |
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|---|---|---|---:|---:|---:| |
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| cylinder | velocity \(u,v\) | \(u,v,p\) | 3990 | 64×128 | 92 / 92 | |
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| controlled_cylinder | \(u,v\) | \(u,v,p\) (+ control params in filenames) | 3990 | 64×128 | 96 / 96 | |
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| fsi | \(u,v\) | \(u,v,p\) | 2173 | 64×64 | 51 / 51 | |
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| foil | \(u,v\) | \(u,v,p\) | 3990 | 64×128 | 98 / 99 | |
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| combustion | OH* chemiluminescence intensity (1 channel) | intensity surrogate (1) + 15 simulated fields | 2001 | 128×128 | 30 / 30 | |
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**Total trajectories** (HDF5 files): **~735** (≈367 real + ≈368 numerical). |
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### Physical parameter ranges (real experiments) |
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| Scenario | Key parameters (real) | |
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|---|---| |
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| cylinder | Reynolds number \(Re\): 1800–12000 | |
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| controlled_cylinder | \(Re\): 1781–9843; control frequency \(f\): 0.5–1.4 Hz | |
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| fsi | \(Re\): 3272–9068; mass ratio \(m^*\): 18.2–20.8 | |
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| foil | angle of attack \(\alpha\): 0°–20°; \(Re\): 2968–17031 | |
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| combustion | CH4 ratio: 20–100%; equivalence ratio \(\phi\): 0.75–1.3 | |
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## Data format on the Hub |
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Each split is stored as a Hugging Face `datasets.Dataset` saved with `Dataset.save_to_disk()`. Concretely, each split is a directory containing: |
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- `data-*.arrow` (sharded Arrow files, float32 payloads stored as bytes) |
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- `dataset_info.json` |
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- `state.json` |
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### test_mode metadata (JSON) |
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RealPDEBench supports `test_mode` evaluation splits (`in_dist`, `out_dist`, `seen`, `unseen`). The group definitions are shipped as JSON dicts per scenario: |
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- `in_dist_test_params_{type}.json` |
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- `out_dist_test_params_{type}.json` |
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- `remain_params_{type}.json` |
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where `{type}` is `real` or `numerical`. |
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### Temporal windowing (what an “example” means) |
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RealPDEBench is stored as **sliding windows** cut from longer trajectories. Each row corresponds to `(sim_id, time_id)`: |
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- `sim_id`: which trajectory (HDF5 file) |
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- `time_id`: start index of the window |
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Typical window lengths \(T\): |
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- **40 frames** for `cylinder`, `fsi`, `foil`, `combustion` (often used as 20‑step input + 20‑step output) |
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- **20 frames** for `controlled_cylinder` (often 10 + 10) |
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- **20 frames** for `combustion/surrogate_train` (surrogate model training data) |
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**Intended layout for the full release** (mirrors the on-disk structure used by RealPDEBench loaders): |
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``` |
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{repo_root}/ |
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cylinder/ |
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in_dist_test_params_real.json |
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out_dist_test_params_real.json |
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remain_params_real.json |
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in_dist_test_params_numerical.json |
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out_dist_test_params_numerical.json |
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remain_params_numerical.json |
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hf_dataset/ |
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real_train/ real_val/ real_test/ |
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numerical_train/ numerical_val/ numerical_test/ |
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fsi/ |
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in_dist_test_params_real.json |
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out_dist_test_params_real.json |
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remain_params_real.json |
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in_dist_test_params_numerical.json |
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out_dist_test_params_numerical.json |
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remain_params_numerical.json |
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hf_dataset/ |
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... |
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combustion/ |
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in_dist_test_params_real.json |
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out_dist_test_params_real.json |
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remain_params_real.json |
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in_dist_test_params_numerical.json |
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out_dist_test_params_numerical.json |
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remain_params_numerical.json |
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hf_dataset/ |
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real_train/ real_val/ real_test/ |
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numerical_train/ # (val/test intentionally empty) |
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surrogate_train/ # combustion-only (surrogate model training) |
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surrogate_train_sim_ids.txt |
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surrogate_train_meta.json |
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... |
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``` |
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### How to download only what you need |
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For large data, use `snapshot_download(..., allow_patterns=...)` to avoid pulling the full repository. |
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```python |
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import os |
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from huggingface_hub import snapshot_download |
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from datasets import load_from_disk |
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repo_id = "AI4Science-WestlakeU/RealPDEBench" |
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os.environ["HF_HUB_DISABLE_XET"] = "1" |
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local_dir = snapshot_download( |
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repo_id=repo_id, |
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repo_type="dataset", |
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allow_patterns=["fsi/**"], # example: download only the FSI folder |
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endpoint="https://hf-mirror.com", |
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) |
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ds = load_from_disk(os.path.join(local_dir, "fsi", "hf_dataset", "numerical_val")) |
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row = ds[0] |
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print(row.keys()) |
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``` |
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## Schema (columns) |
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### Fluid datasets (`cylinder`, `controlled_cylinder`, `fsi`, `foil`) |
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- **Keys**: |
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- `sim_id` (string): trajectory file name (e.g., `10031.h5`) |
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- `time_id` (int): start frame index of the window |
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- `u`, `v` (bytes): float32 arrays of shape `(T, H, W)` |
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- `p` (bytes): float32 array `(T, H, W)` *(numerical splits only)* |
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- `shape_t`, `shape_h`, `shape_w` (int): shapes for decoding |
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### Combustion dataset (`combustion`) |
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- **Keys**: |
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- `sim_id` (string): e.g., `40NH3_1.1.h5` |
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- `time_id` (int): start frame index of the window |
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- `observed` (bytes): float32 array `(T, H, W)` (real: measured intensity; numerical: surrogate intensity) |
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- `numerical` (bytes): float32 array `(T, H, W, 15)` *(numerical splits only)* |
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- `numerical_channels` (int): number of numerical channels (15) |
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- `shape_t`, `shape_h`, `shape_w` (int): shapes for decoding |
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### Combustion surrogate-train (`combustion/surrogate_train`) |
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Used to train a surrogate model mapping simulated modalities → real modality (combustion only). |
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- **Keys**: |
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- `real` (bytes): float32 array `(T, H, W)` (target intensity) |
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- `numerical` (bytes): float32 array `(T, H, W, C)` (input fields) |
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- plus shapes (`*_shape_*`) and `numerical_channels` |
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## Current converted data size (local conversion; full release target) |
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These numbers refer to our current HF Arrow conversion outputs (not all uploaded to this test repo yet): |
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- **Total**: ~**954GB** across all scenarios |
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- **Largest shard file**: ~**0.47GB** (well below the Hub’s recommended **<50GB per file**) |
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- **Total file count**: ~**2.1k files** (well below the Hub’s recommended **<100k files per repo**) |
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Per-scenario totals (HF Arrow): |
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| Scenario | Total size | |
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|---|---:| |
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| combustion | 622GB | |
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| cylinder | 116GB | |
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| fsi | 34GB | |
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| controlled_cylinder | 61GB | |
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| foil | 124GB | |
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## Recommended benchmark protocols |
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RealPDEBench supports three standard training paradigms (all evaluated on **real-world** data): |
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- **Simulated training** (numerical only) |
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- **Real-world training** (real only) |
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- **Simulated pretraining + real finetuning** |
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## License |
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This dataset is released under **CC BY‑NC 4.0** (non‑commercial). Please credit the authors and the benchmark paper when using the dataset. |
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## Citation |
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If you find our work and/or our code useful, please cite us via: |
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```bibtex |
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@misc{hu2026realpdebenchbenchmarkcomplexphysical, |
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title={RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data}, |
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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}, |
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year={2026}, |
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eprint={2601.01829}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2601.01829}, |
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} |
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``` |
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## Contact |
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AI for Scientific Simulation and Discovery Lab, Westlake University |
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Maintainer: `westlake-ai4s` (Hugging Face) |
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Org: `AI4Science-WestlakeU` |
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