Computational Methods for Mean-Field Games

Computational Methods for Mean-Field Games

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.