Deep generative models and physics guided GAN for a shape design

Abstract

When designing a part of machines, it is desired to generate shapes that satisfies performance requirements. For such an aim, deep generative models are used. Generative adversarial network (GAN), variational autoencoders (VAE), and VAEGAN are usually employed. In the present study, we compare those three generative models, and explain the necessity of physics guided generative models.