Effective Dynamics for Stochastic Differential Equations

This lecture covers the effective dynamics for non-reversible stochastic differential equations, focusing on molecular dynamics, ergodic sampling, bottlenecks in MD, coarse-graining, and connection to free-energy. It discusses the breakdown in non-reversible settings, effective drift approximation, and the importance of conditional stationary measures. The lecture concludes with insights on the proof of effective dynamics and ongoing research on time-marginal estimates. Various concepts such as Markovian approximation, Lipschitz growth, and scale-separation are explored in the context of non-reversible dynamics.