Workshop — Numerical methods for Hamilton-Jacobi and mean-field PDEs

The workshop addresses recent advances in the numerical and theoretical analysis of Hamilton-Jacobi equations and mean-field PDEs, which lie at the heart of optimal control, differential games, and the modelling of large-scale interacting systems. These equations are central to many applications in physics, finance, engineering, and the social sciences, yet their analysis and numerical approximation remain challenging due to their nonlinear, degenerate, and often high-dimensional nature. Attention will be given to the development of robust numerical methods with proven convergence properties, including semi-Lagrangian schemes, finite element and viscosity approaches, and recent techniques based on scientific machine learning. The workshop will explore the interplay between analytical theory and computational practice, aiming to connect Hamilton-Jacobi and mean-field models with related fields such as stochastic control and optimal transport. By bringing together researchers from across numerical analysis, PDE theory, applied analysis and probability, the event seeks to stimulate new collaborations and perspectives on both foundational problems and real-world applications.