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README.md

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+---
+license: cc-by-4.0
+tags:
+  - physics
+  - pde
+  - neural-operator
+  - fluid-dynamics
+  - combustion
+  - benchmark
+datasets:
+  - AI4Science-WestlakeU/RealPDEBench
+---
+
+# RealPDEBench Model Checkpoints
+
+Trained model checkpoints for [RealPDEBench](https://github.com/AI4Science-WestlakeU/RealPDEBench), a benchmark for evaluating neural PDE solvers on real-world experimental data.
+
+## Models (10 architectures)
+
+| Model | Type | File Size (per checkpoint) |
+|-------|------|---------------------------|
+| DPOT-L | Transformer | 2.5-2.6 GB |
+| FNO | Spectral | 385M-2.1G |
+| Galerkin Transformer | Transformer | 386-642M |
+| WDNO | Diffusion | 351M-1.4G |
+| DPOT-S | Transformer | 118-159M |
+| U-Net | CNN | 88-89M |
+| CNO | Hybrid | 31M |
+| MWT | Wavelet | 22M |
+| Transolver | Transformer | 17M |
+| DeepONet | Neural Operator | 14M |
+
+## Scenarios (5)
+
+| Scenario | Description |
+|----------|-------------|
+| `cylinder` | Flow past a circular cylinder |
+| `controlled_cylinder` | Actively controlled cylinder flow |
+| `fsi` | Fluid-structure interaction |
+| `foil` | Flow past an airfoil |
+| `combustion` | Turbulent combustion |
+
+## Training Paradigms
+
+| File | Paradigm |
+|------|----------|
+| `numerical.pth` | Trained on numerical simulation data only |
+| `real.pth` | Trained on real experimental data only |
+| `finetune.pth` | Pretrained on numerical, finetuned on real |
+| `numerical_base_for_finetune.pth` | Numerical pretrain base (DPOT-S/L only) |
+
+## Directory Structure
+
+```
+{scenario}/{model}/{paradigm}.pth
+configs/{scenario}/{model}.yaml
+```
+
+Example: `cylinder/fno/finetune.pth` + `configs/cylinder/fno.yaml`
+
+## Quick Start
+
+### Install
+
+```bash
+git clone https://github.com/AI4Science-WestlakeU/RealPDEBench.git
+cd RealPDEBench && pip install -e .
+```
+
+### Download a Single Checkpoint
+
+```python
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="AI4Science-WestlakeU/RealPDEBench-models",
+    filename="cylinder/fno/finetune.pth",
+)
+```
+
+### Download All Checkpoints for a Scenario
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="AI4Science-WestlakeU/RealPDEBench-models",
+    allow_patterns="cylinder/**",
+    local_dir="./checkpoints",
+)
+```
+
+### Evaluate
+
+```bash
+python eval.py --config configs/cylinder/fno.yaml \
+    --checkpoint_path ./checkpoints/cylinder/fno/finetune.pth \
+    --dataset_type real --test_mode all
+```
+
+## Checkpoint Format
+
+```python
+checkpoint = torch.load("cylinder/fno/finetune.pth")
+# Keys: model_state_dict, train_losses, val_losses,
+#        iteration, best_iteration, best_val_loss
+```
+
+## DPOT Pretrained Weights
+
+DPOT models require pretrained backbone weights (**not included here**).
+Download via:
+
+```bash
+# Option 1: Built-in download script
+python -m realpdebench.utils.dpot_ckpts_dl
+
+# Option 2: From HuggingFace directly
+# https://huggingface.co/hzk17/DPOT
+```
+
+## Dataset
+
+The corresponding dataset is hosted at: [AI4Science-WestlakeU/RealPDEBench](https://huggingface.co/datasets/AI4Science-WestlakeU/RealPDEBench)
+
+## Citation
+
+```bibtex
+@article{deng2025realpdebench,
+  title={Can Neural Operators Always Be Trusted? A Benchmark for Real-World PDE Solving},
+  author={Deng, Wenhao and Wu, Tailin and Feng, Jiawei and others},
+  year={2025}
+}
+```
+
+## License
+
+CC BY 4.0