AI for Stochastic Dynamics: From Theoretical Foundations to Scientific Applications

Stochastic analysis and scientific machine learning meet around models of random dynamics. Topics include suitable learning objectives, preserving stochastic structure in numerical methods, and evaluating learned systems in applications ranging from molecular and biological processes to fluids, climate and control. Session: 08:00–17:00 (Australia/Sydney). A paid NeurIPS virtual pass includes workshop livestreams; on-site access follows the host-location registration rules.