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 ...

--Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a hierarchical scheme for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. We developed a highly optimized implementation of this scheme that scales to 27,500 NVIDIA V olta GPUs and 4584 nodes on the Summit supercomputer with a 93.1% scaling efficiency, achieving peak and sustained half-precision rates of 1228 PF/s and 1207 PF/s. Index T erms --Stochastic PDEs, GANs, Deep Learning I. O VERVIEW A. Parameter estimation and uncertainty quantification for subsurface flow models Mathematical models of subsurface flow and transport are inherently uncertain because of the lack of data about the distribution of geological units, the distribution of hydrological properties (e.g., hydraulic conductivity) within each unit, and initial and boundary conditions. Here, we focus on parameter-ization and uncertainty quantification (UQ) in the subsurface flow model at the Department of Energy's Hanford Site, one of the most contaminated sites in the western hemisphere. During the Hanford Site's 60-plus years history, there have been more than 1000 individual sources of contaminants distributed over 200 square miles mostly along Columbia River [1]. Accurate subsurface flow models with rigorous UQ are necessary for assessing risks of the contaminants reaching the Columbia river and numerous wells used by agriculture and as sources of drinking water, as well as for the design of efficient remediation strategies. B. UQ with Stochastic Partial Differential Equations Uncertain initial and boundary conditions and model parameters render the governing model equations stochastic. In this context, UQ becomes equivalent to solving stochastic PDEs (SPDEs). Forward solution of SPDEs requires that all model parameters as well as the initial/boundary conditions are prescribed either deterministically or stochastically, which is not possible unless experimental data are available to provide additional information for critical parameters, e.g. the field conductivity.

File photo - An airplane flies over a drone during the Polar Bear Plunge on Coney Island in the Brooklyn borough of New York Jan. 1, 2015. While it seems unlikely that everyday drone hobbyists would want to make a beeline for their nearest nuclear facility to grab some aerial shots, the Federal Aviation Administration (FAA) has nevertheless announced a ban on drone flights over such locations in the U.S., namely: As you can see, they're mainly labs, while the Hanford Site, for example, is a mostly decommissioned nuclear production complex. Another of those listed, the Pantex Site, is an active nuclear weapons assembly and dismantlement plant. The restrictions, which come into force on December 29, have been put in place "to address concerns about unauthorized drone operations over seven Department of Energy (DOE) facilities," the FAA confirmed on its website. It added that "operators who violate the airspace restrictions may be subject to enforcement action, including potential civil penalties and criminal charges."