StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction

Abstract

Using deep learning to analyze mechanical stress distributions is gaining interest with the demand for fast stress analysis. Deep learning approaches have achieved excellent outcomes when utilized to speed up stress computation and learn the physical nature without prior knowledge of underlying equations. However, most studies restrict the variation of geometry or boundary conditions, making it difficult to generalize the methods to unseen configurations. We propose a conditional generative adversarial network (cGAN) model called StressGAN for predicting 2D von Mises stress distributions in solid structures. The StressGAN model learns to generate stress distributions conditioned by geometries, loads, and boundary conditions through a two-player minimax game between two neural networks with no prior knowledge. By evaluating the generative network on two stress distribution datasets under multiple metrics, we demonstrate that our model can predict more accurate stress distributions than a baseline convolutional neural-network model, given various and complex cases of geometries, loads, and boundary conditions.