  Using deep learning to analyze mechanical stress distributions has been
gaining interest with the demand for fast stress analysis methods. Deep
learning approaches have achieved excellent outcomes when utilized to speed up
stress computation and learn the physics without prior knowledge of underlying
equations. However, most studies restrict the variation of geometry or boundary
conditions, making these methods difficult to be generalized to unseen
configurations. We propose a conditional generative adversarial network (cGAN)
model for predicting 2D von Mises stress distributions in solid structures. The
cGAN learns to generate stress distributions conditioned by geometries, load,
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 high-resolution stress distributions than a
baseline convolutional neural network model, given various and complex cases of
geometry, load and boundary conditions.
