---
language:
- en
tags:
- OneScience
- fluid dynamics
- large-scale fluid dynamics benchmark
frameworks: PyTorch
---

<p align="center">
  <strong>
    <span style="font-size: 30px;">CFDBench</span>
  </strong>
</p>

# Model Overview

CFDBench is a large-scale benchmark developed by researchers at Tsinghua University to evaluate machine-learning methods for computational fluid dynamics. It focuses on model generalization across different boundary conditions, fluid properties, and geometric configurations.

Paper: [CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://arxiv.org/abs/2310.05963)

# Model Description
CFDBench is built from several representative computational fluid dynamics datasets spanning diverse boundary conditions, fluid properties, and geometries. It evaluates both flow-field prediction performance and the generalization capabilities of machine-learning methods.


# Use Cases

| Use Case | Description |
| :---: | :---: |
| CFD surrogate benchmarking | Compare the flow-field prediction capabilities of neural operators and other deep-learning models using consistent data splits and metrics |
| Autoregressive flow evolution | Predict subsequent two-dimensional velocity fields step by step from the current grid-based field and assess error accumulation over multiple steps |
| Non-autoregressive field queries | Predict velocity directly at target locations from operating parameters and spatiotemporal coordinates, enabling evaluation over long time horizons |


## Supported Models

| Type | `root.model.name` | Training Entry Point |
| :--- | :--- | :--- |
| Non-autoregressive | `ffn` | `python scripts/train.py` |
| Non-autoregressive | `deeponet` | `python scripts/train.py` |
| Autoregressive | `auto_ffn` | `python scripts/train_auto.py` |
| Autoregressive | `auto_deeponet` | `python scripts/train_auto.py` |
| Autoregressive | `auto_edeeponet` | `python scripts/train_auto.py` |
| Autoregressive | `auto_deeponet_cnn` | `python scripts/train_auto.py` |
| Autoregressive | `resnet` | `python scripts/train_auto.py` |
| Autoregressive | `unet` | `python scripts/train_auto.py` |
| Autoregressive | `fno` | `python scripts/train_auto.py` |

# 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
```
modelscope download --model OneScience/CFDBench --local_dir ./CFDBench
cd CFDBench
```
### 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

The OneScience community provides the `cfdbench` dataset for training. Download it with the command below and verify that the data path in `config/config.yaml` is configured correctly:

```bash
modelscope download --dataset OneScience/cfdbench --local_dir ./data
```

### Training
**Autoregressive Training**

The default configuration sets `root.model.name: fno`, so use the autoregressive training entry point:

```bash
python scripts/train_auto.py
```


To select another autoregressive model, edit `conf/config.yaml`:

```yaml
root:
  model:
    name: "auto_ffn"
```

**Non-autoregressive Training**
```bash
python scripts/train.py --model deeponet
```
### Model Weights
This repository will provide pretrained CFDBench model weights in the `weights/` directory. The weights will be uploaded soon.

### Inference and Visualization

The inference script automatically selects the task type from the current model name and reads the following path by default:

```text
./weight/<model.name>.pt
```

Default FNO inference and visualization:

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

Non-autoregressive models can likewise be selected through a command-line argument:

```bash
python scripts/inference.py --model ffn
python scripts/result.py --model ffn
```
# 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

- Original CFDBench paper: [CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://arxiv.org/abs/2310.05963)
- Original CFDBench code repository: https://github.com/luo-yining/CFDBench
- This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope. Before public redistribution, verify the applicable licensing requirements of the upstream project.