README.md

---
frameworks:
- ""
language:
- en
license: apache-2.0
tags:
- OneScience
- fluid dynamics
- neural PDE solver evaluation
- unstructured-mesh simulation
- multiphysics modeling
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">CFD_Benchmark</span>
  </strong>
</p>

# Model Overview
CFD_Benchmark is an open-source deep-learning benchmark library for research on neural partial differential equation (PDE) solvers. It extends Tsinghua University's open-source Neural-Solver-Library with Distributed Data Parallel (DDP) training support, additional models, and new datasets, while retaining the original neural-operator and physical-field modeling framework. The library supports neural PDE solver evaluation, deep-learning research for CFD, multi-model performance comparisons, large-scale distributed training experiments, physical simulation dataset development, and algorithm benchmarking.

The library currently supports the following benchmarks:

- Six standard benchmarks from [[FNO]](https://arxiv.org/abs/2010.08895) and [[geo-FNO]](https://arxiv.org/abs/2207.05209)
- PDEBench [[NeurIPS 2022 Track dataset and benchmark]](https://arxiv.org/abs/2210.07182) for autoregressive tasks
- The ShapeNet-Car dataset [[TOG 2018]](https://dl.acm.org/doi/abs/10.1145/3197517.3201325) for industrial design benchmarks
- The BubbleML [[Multiphase Multiphysics Dataset]](https://arxiv.org/abs/2307.14623) for studying multiphysics phase-transition phenomena

---

## Supported Neural Solvers

The following neural PDE solvers are supported:


- **Transolver** - Transolver: A Fast Transformer Solver for PDEs on General Geometries [[ICML 2024]](https://arxiv.org/abs/2402.02366) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Transolver.py)
- **ONO** - Improved Operator Learning by Orthogonal Attention [[ICML 2024]](https://arxiv.org/abs/2310.12487v3) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/ONO.py)
- **Factformer** - Scalable Transformer for PDE Surrogate Modeling [[NeurIPS 2023]](https://arxiv.org/abs/2305.17560) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Factformer.py)
- **U-NO** - U-NO: U-shaped Neural Operators [[TMLR 2023]](https://openreview.net/pdf?id=j3oQF9coJd) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/U_NO.py)
- **LSM** - Solving High-Dimensional PDEs with Latent Spectral Models [[ICML 2023]](https://arxiv.org/pdf/2301.12664) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/LSM.py)
- **GNOT** - GNOT: A General Neural Operator Transformer for Operator Learning [[ICML 2023]](https://arxiv.org/abs/2302.14376) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/GNOT.py)
- **F-FNO** - Factorized Fourier Neural Operators [[ICLR 2023]](https://arxiv.org/abs/2111.13802) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/F_FNO.py)
- **U-FNO** - An enhanced Fourier neural operator-based deep-learning model for multiphase flow [[Advances in Water Resources 2022]](https://www.sciencedirect.com/science/article/pii/S0309170822000562) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/U_FNO.py)
- **Galerkin Transformer** - Choose a Transformer: Fourier or Galerkin [[NeurIPS 2021]](https://arxiv.org/abs/2105.14995) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Galerkin_Transformer.py)
- **MWT** - Multiwavelet-based Operator Learning for Differential Equations [[NeurIPS 2021]](https://openreview.net/forum?id=LZDiWaC9CGL) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/MWT.py)
- **FNO** - Fourier Neural Operator for Parametric Partial Differential Equations [[ICLR 2021]](https://arxiv.org/pdf/2010.08895) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/FNO.py)
- **Transformer** - Attention Is All You Need [[NeurIPS 2017]](https://arxiv.org/pdf/1706.03762) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Transformer.py)

- **GFNO** - Group Equivariant Fourier Neural Operators for Partial Differential Equations[[2023 Poster]](https://arxiv.org/pdf/1706.03762)[[Code]](https://github.com/divelab/AIRS/blob/main/OpenPDE/G-FNO/models/GFNO.py)

Several vision architectures also serve as effective baselines for structured-geometry tasks:

- **Swin Transformer** - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows [[ICCV 2021]](https://arxiv.org/abs/2103.14030) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Swin_Transformer.py)
- **U-Net** - U-Net: Convolutional Networks for Biomedical Image Segmentation [[MICCAI 2015]](https://arxiv.org/pdf/1505.04597) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/U_Net.py)

Several established geometric deep-learning models are included for design tasks:

- **Graph-UNet** - Graph U-Nets [[ICML 2019]](https://arxiv.org/pdf/1905.05178) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/Graph_UNet.py)
- **GraphSAGE** - Inductive Representation Learning on Large Graphs [[NeurIPS 2017]](https://arxiv.org/pdf/1706.02216) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/GraphSAGE.py)
- **PointNet** - PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation [[CVPR 2017]](https://arxiv.org/pdf/1612.00593) [[Code]](https://github.com/thuml/Neural-Solver-Library/blob/main/models/PointNet.py)

The library also includes the following graph neural network:

- **MeshGraphNet** LEARNING MESH-BASED SIMULATION WITH GRAPH NETWORKS[ICLR 2021](https://arxiv.org/abs/2010.03409) [[Code]](https://github.com/google-deepmind/deepmind-research/tree/master/meshgraphnets)



## Use Cases

| Use Case | Description |
|---|---|
| Neural PDE solver evaluation | Train, run inference with, and compare models such as FNO, Transolver, GNOT, ONO, and U-NO through a unified workflow |
| Autoregressive physical prediction | Predict the temporal evolution of PDE states step by step using datasets such as PDEBench |
| Multiphysics modeling | Study multiphase flows, multiphysics coupling, and phase-transition phenomena using datasets such as BubbleML |
| ModelScope/OneCode execution | Download the standalone model package, install its dependencies, and run the provided scripts |


# Usage

## 1. OneCode

Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience:

[Launch OneCode for one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Setup

**Hardware Requirements**

- A GPU or DCU is recommended.
- A CPU can be used for import checks and small-scale pipeline validation, but full training and inference will be slow.
- DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster, is recommended.


### Download the Model Package

```bash
modelscope download --model OneScience/CFD_Benchmark --local_dir ./CFD_Benchmark 
cd CFD_Benchmark 
```

### Set Up the Runtime Environment


**DCU Environment**

```bash
# Activate DTK and Conda first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# Installation with uv is also supported
pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

**GPU Environment**
```bash
# Activate Conda first
conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
# Installation with uv is also supported
pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

### Training Data

Use the dataset links in the benchmark overview above to download the required data.

The six standard benchmark datasets from [[FNO]](https://arxiv.org/abs/2010.08895) and [[geo-FNO]](https://arxiv.org/abs/2207.05209) are available from [this link](https://drive.google.com/drive/folders/1YBuaoTdOSr_qzaow-G-iwvbUI7fiUzu8).

The PDEBench [[NeurIPS 2022 Track dataset and benchmark]](https://arxiv.org/abs/2210.07182), used for benchmarking autoregressive tasks, is available from [this link](https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986).

The ShapeNet-Car [[TOG 2018]](https://dl.acm.org/doi/abs/10.1145/3197517.3201325) benchmark dataset for industrial design tasks is available from [[this link]](http://www.nobuyuki-umetani.com/publication/mlcfd_data.zip).

The BubbleML [[Multiphase Multiphysics Dataset]](https://arxiv.org/abs/2307.14623), designed for research on multiphysics phase-transition phenomena, is available from [[this link]](https://github.com/HPCForge/BubbleML/blob/main/bubbleml_data/README.md).

The OneScience community also provides the `cfd_benchmark` dataset for training. Download it with the command below and verify that the data path in `conf/config.yaml` is configured correctly:
```
modelscope download --dataset OneScience/cfd_benchmark  --local_dir ./data
```
### Training

```bash
python scripts/train.py
```

### Model Weights
This repository will provide weights trained on the OneScience `cfd_benchmark` dataset in the `weights/` directory. The weights will be uploaded soon.

### Inference

```bash
python scripts/inference.py
```

Inference loads the trained weights referenced by `paths.weight_path` and writes the metrics to:

```text
./results/{train.save_name}/metrics.json
```


# Official OneScience Resources

| Platform | OneScience Repository | Skills Repository |
| --- | --- | --- |
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |

# Citations and License
- Reference repository: [Neural-Solver-Library](https://github.com/thuml/Neural-Solver-Library).
- This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope.