IMA Data Science Seminar: Machine Learning Methods for Solving High-dimensional Mean-field Game Systems
Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.
This week, Levon Nurbekyan (University of California, Los Angeles), will be giving a talk titled "Machine Learning Methods for Solving High-dimensional Mean-field Game Systems".
Registration is required to access the Zoom webinar.
Abstract
Mean-field games (MFG) is a framework to model and analyze huge populations of interacting agents that play non-cooperative differential games with applications in crowd motion, economics, finance, etc. Additionally, the PDE that arise in MFG have a rich mathematical structure and include those that appear in optimal transportation and density flow problems. In this talk, I will discuss applications of machine-learning techniques to solve high-dimensional MFG systems. I will present Lagrangian, GAN-type, and kernel-based methods for suitable types of MFG systems.