Experiment code associated with the paper: Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
This code is licensed under the MIT license. Feel free to use all or portions for your research or related projects so long as you provide the following citation information:
Wei Chen, Kevin Chiu, and Mark Fuge. "Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks." AIAA Journal (2020) Accepted.
@article{chen2020airfoil,
title={Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks},
author={Chen, Wei and Chiu, Kevin and Fuge, Mark},
journal={AIAA Journal},
volume={},
number={},
pages={},
year={2020},
publisher={American Institute of Aeronautics and Astronautics}
}
- tensorflow<2.0.0
- pyDOE
- sklearn
- numpy
- matplotlib
- autograd
Go to Bézier-GAN's directory:
cd beziergan
Train a Bézier-GAN or evaluate a trained Bézier-GAN:
python train_gan.py
positional arguments:
mode train or evaluate
latent latent dimension
noise noise dimension
optional arguments:
-h, --help show this help message and exit
--model_id model ID
--save_interval number of intervals for saving the trained model and plotting results
The trained model and synthesized shape plots will be saved under directory beziergan/trained_gan/<latent>_<noise>/<model_id>
, where <latent>
, <noise>
, and <model_id>
are latent dimension, noise dimension, and model ID specified in the above arguments.
python optimize_gan_bo_refine.py
positional arguments:
latent latent dimension
noise noise dimension
optional arguments:
--n_runs number of experiment runs
--n_eval number of evaluations per run
-h, --help show this help message and exit
python optimize_gan_bo.py
positional arguments:
latent latent dimension
noise noise dimension
optional arguments:
--n_runs number of experiment runs
--n_eval number of evaluations per run
-h, --help show this help message and exit
Optimization history and optimal airfoil shapes are saved under directory results_opt
.
Our airfoil designs come from UIUC airfoil coordinates database.
The raw data contains variable number of points along airfoil curves. We created the training data by applying B-spline interpolation on these designs and removed outlier designs.
Randomly generated airfoils:
Optimization history:
Optimized airfoils: