Dynamics and Control Out of Equilibrium: From Active to Learning Systems

Nonequilibrium systems are ubiquitous, from swarms of living organisms to machine learning algorithms. While much of statistical physics has focused on predicting emergent behavior from microscopic rules, a growing question is the inverse problem: how can we guide a nonequilibrium system toward a desired state? This challenge becomes particularly daunting in high-dimensional or complex systems, where classical control approaches often break down. In this talk, I will integrate methods from optimal control theory with techniques from soft matter and statistical physics to tackle this problem in two broad classes of nonequilibrium systems: active matter—focusing on multimodal strategies in animal navigation and mechanical confinement of active fluids—and learning systems, where I will apply control theory to identify optimal learning principles for neural networks. Together, these approaches point toward a general framework for controlling nonequilibrium dynamics across systems and scales.