Computational Methods for Mean-Field Games
Offered By: Institute for Pure & Applied Mathematics (IPAM) via YouTube
Course Description
Overview
Dive into the first part of a comprehensive lecture on computational methods for mean-field games, presented by Levon Nurbekyan from the University of California, Los Angeles. Explore finite-difference, convex optimization, Lagrangian, and cutting-edge machine-learning techniques for solving mean-field game systems. Gain valuable insights into this complex topic as part of the High Dimensional Hamilton-Jacobi PDEs Tutorials 2020 series, hosted by the Institute for Pure and Applied Mathematics at UCLA. Enhance your understanding of these advanced mathematical concepts and their applications in this 1-hour 17-minute presentation.
Syllabus
Levon Nurbekyan: "Computational methods for mean-field games (Part 1/2)"
Taught by
Institute for Pure & Applied Mathematics (IPAM)
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