Deep Learning Infiltrating HPC Physics Domains

While deep learning models might not be able to simulate large-scale physical phenomena in the same way purpose-built supercomputers and their application stacks do, there is more research emerging that shows how traditional HPC simulations can be augmented, if not replaced in some parts, by neural networks. An upcoming meeting of the American Physical Society that will focus on fluid dynamics and turbulence will shed light on how and where this happening with a number of presentations focused on how neural nets fit into CFD and other physics-driven simulation areas. Researchers from Los Alamos National Lab compared three deep learning models, generative adversarial networks, LAT-NET, and LSTM against their own observations about homogeneous, isotropic, and stationary turbulence and found that deep learning, "which do not take into account any physics of turbulence explicitly, are impressively good overall when it comes to qualitative description of important features of turbulence." Even still, they add that there are some shortcomings that can be addressed by making corrections to the deep learning frameworks through reinforcement of special features of turbulence that the models do not pick out on their own after training. They will present results from a fully-trained GAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence.