DATE:

Friday, April 8, 2022

TIME:

1:00 PM

LOCATION:

SDSU Alumni Center

SPEAKER:

Brian Spears, Physicist, Lawrence Livermore National Laboratory

ABSTRACT:

Large-scale scientific endeavors often focus on improving predictive capabilities by challenging theory-driven simulations with experimental data. Yet, both simulation and experiment have become overwhelmingly rich,

with a complex of observables including scalars, vector-valued data, and various images. At Lawrence Livermore National Laboratory (LLNL), we are using modern artificial intelligence (AI) technologies to combine predictive simulation models with rich experimental data. We call this set of methods cognitive simulation. We will describe a strategic LLNL research effort aimed at using recent advances in deep learning, computational workflows, and computer architectures to develop improved predictive models. We will present work from a wide range of applications, including using inertial confinement fusion research and experiments at the world’s largest laser, the National Ignition Facility (NIF), as a testbed. We will describe advances in machine learning architectures and methods necessary to handle strong nonlinearities and multimodal data in ICF science and other applications. We will also cover state-of-the-art tools that we developed to steer physics simulation, uncertainty quantification, and model training at enormous scale using state-of-the-art supercomputers and AI accelerators. We finally describe our ongoing efforts to advance deep learning for applied science using academic and industrial partnerships steered by our AI Innovation Incubator (AI3).

HOST:

Computational Science Research Center

VIDEO: