https://github.com/zjowowen/funcgenfoil
Airfoil Generation and Editing Model in Function Space
https://github.com/zjowowen/funcgenfoil
function-space-modeling generative-ai generative-design machine-learning
Last synced: 9 months ago
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Airfoil Generation and Editing Model in Function Space
- Host: GitHub
- URL: https://github.com/zjowowen/funcgenfoil
- Owner: zjowowen
- License: apache-2.0
- Created: 2025-02-15T08:08:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-08-13T09:42:24.000Z (11 months ago)
- Last Synced: 2025-09-04T04:54:24.240Z (10 months ago)
- Topics: function-space-modeling, generative-ai, generative-design, machine-learning
- Language: Python
- Homepage:
- Size: 7.04 MB
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# FuncGenFoil: Airfoil Generation and Editing Model in Function Space
[](https://arxiv.org/abs/2502.10712)
[](https://opensource.org/licenses/Apache-2.0)
English | [简体中文(Simplified Chinese)](https://github.com/zjowowen/FuncGenFoil/blob/main/README.zh.md)
**FuncGenFoil**, short for Function-Space Generated Airfoil, is a method for generating airfoils using generative models in function space, such as diffusion models or flow models. This library provides a framework to demonstrate the power of generative models in design and optimization.
## Outline
- [FuncGenFoil: Airfoil Generation and Editing Model in Function Space](#funcgenfoil-airfoil-generation-and-editing-model-in-function-space)
- [Outline](#outline)
- [Framework](#framework)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Citation](#citation)
- [License](#license)
## Framework
Demonstration of airfoil generation model training and inference in function space.
Demonstration of airfoil editing model training and inference in function space.
## Installation
Please install from source:
```bash
git clone https://github.com/zjowowen/FuncGenFoil.git
cd FuncGenFoil
pip install -e .
```
## Quick Start
Here is an example of how to train a airfoil generative flow model in function space.
Download dataset from [here](https://drive.google.com/drive/folders/1LU6p-TeWpH5b1Vvh2GRv_TwetHkyV8jZ?usp=sharing) and save it in the current directory.
To train the model without conditional information:
```bash
python examples/train_unconditional_airfoil_generation.py
```
To train the model with conditional information:
```bash
python examples/train_conditional_airfoil_generation.py
```
To train the model for airfoil editing:
```bash
python examples/train_airfoil_editing.py
```
To evaluate the model for airfoil generation with super-resolution:
```bash
python examples/eval_airfoil_generation_super_resolution.py
```
To evaluate airfoil generation and editing using Gradio:
```bash
pip install gradio
python app.py
```
## Citation
If you find this work useful, please consider citing:
```latex
@misc{zhang2025funcgenfoilairfoilgenerationediting,
title={FuncGenFoil: Airfoil Generation and Editing Model in Function Space},
author={Jinouwen Zhang and Junjie Ren and Aobo Yang and Yan Lu and Lu Chen and Hairun Xie and Jing Wang and Miao Zhang and Wanli Ouyang and Shixiang Tang},
year={2025},
eprint={2502.10712},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2502.10712},
}
```
## License
FuncGenFoil is licensed under the Apache License 2.0. See [LICENSE](LICENSE) for more details.