---
license: apache-2.0
language:
- en
tags:
- OneScience
- fluid dynamics
- airfoil aerodynamic prediction
frameworks: PyTorch
---
<p align="center">
  <strong>
    <span style="font-size: 30px;">Transolver-Airfoil-Design</span>
  </strong>
</p>

# Model Overview

Transolver-Airfoil-Design is a two-dimensional external-flow prediction model for airfoils, built on Transolver by Tsinghua University's THUML group. It rapidly predicts flow-field distributions and aerodynamic performance around airfoils.

Paper: [Transolver: A Fast Transformer Solver for PDEs on General Geometries](https://arxiv.org/abs/2402.02366)

# Model Description
Transolver-Airfoil-Design uses a Transformer architecture with Physics-Attention and is adapted for training on the unstructured-mesh AirfRANS airfoil dataset. It predicts velocity fields, pressure fields, and drag coefficients for airfoil geometries.

## Use Cases

| Use Case | Description |
| :--- | :--- |
| Airfoil aerodynamic design | Rapidly predict two-dimensional external flow around airfoils to screen candidate geometries |
| Industrial simulation acceleration | Accelerate large-scale simulations using PDE surrogate modeling on complex geometries |

# 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/Transolver-Airfoil-Design --local_dir ./Transolver-Airfoil-Design
cd Transolver-Airfoil-Design
```

### 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 `airfrans` 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/airfrans --local_dir ./data
```

### Training

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

By default, training saves the following checkpoint:

```text
./weight/Transolver.pth
```

### Model Weights
This repository will provide model weights pretrained on ShapeNetCar data in the `weights/` directory. The weights will be uploaded soon.

### Inference

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

### Evaluation and Visualization

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

# 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 Transolver paper: [Transolver: A Fast Transformer Solver for PDEs on General Geometries](https://arxiv.org/pdf/2402.02366).
- This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope.