Deep Learning on Summit Supercomputer Powers Insights for Nuclear Waste Remediation - insideHPC

A research collaboration between LBNL, PNNL, Brown University, and NVIDIA has achieved exaflop (half-precision) 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. In science we know the laws of physics and observation principles – mass, momentum, energy, etc.," said George Karniadakis, professor of applied mathematics at Brown and co-author on the SC19 workshop paper. "The concept of physics-informed GANs is to encode prior information from the 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 ...