Recorded 16 July 2025. Jiajia Yu of the University of California, Los Angeles, presents "Learning and Inference in Mean-Field Games" at IPAM's Sampling, Inference, and Data-Driven Physical Modeling in Scientific Machine Learning Workshop. Abstract: Mean-Field Games (MFGs) study the Nash equilibrium of non-cooperative games involving a continuum of players. They have broad applications and deep connections to areas such as sampling, optimal transport and economics, etc. In this talk, I will present our recent works in both forward and inverse problems in MFGs, with insights gained through numerical analysis and computational methods. I will begin by presenting a convergence analysis of a learning algorithm for MFGs. Our results highlight the central role of the best response in understanding both the game dynamics and the algorithm behavior. Then, I will introduce a simple and efficient iterative strategy for solving a class of inverse MFG problems. This approach shows that measurements of the Nash equilibrium state can be remarkably effective in inferring unknown ambient potentials, such as obstacles. This talk is based on joint works with Xiuyuan Cheng, Jian-Guo Liu and Hongkai Zhao. Learn more online at: https://www.ipam.ucla.edu/programs/workshops/sampling-inference-and-data-driven-physical-modeling-in-scientific-machine-learning-2/