We establish a connection between stochastic optimal control and generative models based
on stochastic differential equations (SDEs), such as recently developed diffusion …

Recently, a series of papers proposed deep learning-based approaches to sample from
target distributions using controlled diffusion processes, being trained only on the …

Optimal control of diffusion processes is intimately connected to the problem of solving
certain Hamilton–Jacobi–Bellman equations. Building on recent machine learning inspired …

One of the main challenges in molecular dynamics is overcoming the 'timescale barrier': in
many realistic molecular systems, biologically important rare transitions occur on timescales …

Stochastic optimal control, which has the goal of driving the behavior of noisy systems, is
broadly applicable in science, engineering and artificial intelligence. Our work introduces …

Decision making under uncertainty is critical to real-world, autonomous systems. Model
Predictive Control (MPC) methods have demonstrated favorable performance in practice …

In this work we explore a new framework for approximate Bayesian inference in large
datasets based on stochastic control. We advocate stochastic control as a finite time and low …

Data assimilation addresses the general problem of how to combine model-based
predictions with partial and noisy observations of the process in an optimal manner. This …

Abstract The combination of Monte Carlo methods and deep learning has recently led to
efficient algorithms for solving partial differential equations (PDEs) in high dimensions …

We consider damped stochastic systems in a controlled (time varying) potential and study
their transition between specified Gibbs-equilibria states in finite time. By the second law of …