Deep Learning Expands Study of Nuclear Waste Remediation

A research collaboration between Lawrence Berkeley National Laboratory (Berkeley Lab), Pacific Northwest National Laboratory (PNNL), Brown University, and NVIDIA 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.

“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 appearance with remarkable accuracy, noted Prabhat, a co-author on the SC19 paper who leads the Data and Analytics Services team at Berkeley Lab’s National Energy Research Scientific Computing Center. “In science, Berkeley Lab has explored the application of vanilla GANs for creating synthetic universes and particle physics experiments; one of the open challenges thus far has been the incorporation of physical constraints into the predictions,” he said. “George and his group at Brown have pioneered the approach of incorporating physics into GANs and using them to synthesize data – in this case, subsurface flow fields.”

Read more at DOE / Lawrence Berkeley National Laboratory

Image: The PI-GAN architecture used in the study.  CREDIT: Lawrence Berkeley National Laboratory