Trainees will be acquainted with physics-informed machine learning principles and applications, including physics-informed neural networks (PINNs) to solve forward and inverse problems; as well as their use to solve partial differential equations with a variety of boundary conditions.
Time: February 3, 3-5 p.m. US CT
This session is a part of the ALCF AI for Science Training Series.
About the Speaker
Shawn Rosofsky is a graduate student at the University of Illinois at Urbana-Champaign pursuing a PhD in physics. His research involves simulating gravitational wave sources to help detectors such as LIGO identify and interpret the gravitational wave signals they observe. Shawn's current work is with the National Center for Supercomputing Applications' (NCSA) Gravity Group.