Development-of-Variational-Autoencoder-VAE-and-Generative-Adversarial-Network-GAN-

This repository contains the implementation of a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN) using PyTorch for the generation of airfoil shapes. The models are trained on the UIUCAirfoil Coordinates Database, which includes coordinates for nearly 1,600 airfoils.

Features

1. VAE Implementation: The repository includes a VAE model implemented in PyTorch. The VAE consists of an Encoder, which encodes the input airfoil coordinates into a low-dimensional latent space, and a Decoder, which reconstructs the airfoil coordinates from the latent space.

2. GAN Implementation: Additionally, the repository contains a GAN model implemented in PyTorch. The GAN consists of a Generator, which generates synthetic airfoil coordinates from random noise, and a Discriminator, which discriminates between real and fake airfoils.

Usage

1. Clone the repository to your local machine:git clone https://github.com/your-username/airfoil-generation-via-VAE-GAN.git

2. Set up your Python environment and install the required dependencies listed in the requirements.txt file.

3. Modify hyperparameters, if necessary, in the model files and training scripts to suit your specific requirements.

Contributing

Contributions to this repository are welcome. If you have suggestions for improvements or new features, please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Carnegie Mellon University (CMU) License

This software is distributed under the terms of the CMU License. See the LICENSE_CMU file for details.

References

• PyTorch VAE implementation: pytorch/examples/vae
• PyTorch GAN implementation: eriklindernoren/PyTorch-GAN