Deep Learning Expands Study Of Nuclear Waste Remediation - Pioneering Minds

A research collaboration has achieved exaflop performance on the Summit supercomputer with a deep learning application used to model subsurface flow in the study of nuclear waste remediation. Their achievement, which will be presented during the "Deep Learning on Supercomputers" workshop at SC19, demonstrates the promise of physics-informed generative adversarial networks (GANs) for analyzing complex, large-scale science problems. The concept of physics-informed GANs is to encode prior information from physics into the neural network. This allows you to go well beyond the training domain, which is very important in applications where the conditions can change. GANs have been applied to model human face appearance with remarkable accuracy.