Deep Learning Methods in Mathematical Physics
Speaker: Prof. Ovidiu Calin, Department of Mathematics & Statistics, Eastern Michigan University
Host: S Baskar
Title: Hamilton-Jacobi Theory
Time, day and date: 11:30:00 AM – 12:30:00 PM, Tuesday, February 24
Venue: Ramanujan Hall
Abstract: This lecture presents the Hamilton–Jacobi theory from a neural-network perspective. We study how neural networks can be used to solve the Hamilton–Jacobi equation and recover system dynamics, with applications to the eikonal equation and distance computation. The lecture highlights neural-network-based approaches to solving first-order nonlinear PDEs and demonstrates how Riemannian distances and optimal trajectories can be computed in a data-driven framework.