Week 07.04.2025 – 13.04.2025

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.

Active filaments, such as kinesin propelled microtubules in gliding assay experiments, give rise to a plethora of active phases. In order to better understand which features of these phases are emergent and which exist at even the single filament level, we investigate the dynamics of individual active elastic filaments with chiral self-propulsion. To this end, we study the fully general time evolution of an overdamped plane curve and derive equations for the evolution of the curve’s shape and orientational characteristics. Applying this formalism to the specific case of an active elastic filament with chiral self-propulsion, we determine that sufficiently flexible filaments can exhibit stationary states with shape multi-stability which in turn gives rise to rotational dynamics. Further, the time-dependent evolution towards such steady states is highly nontrivial with both wave-like and diffusive characteristics available depending on the elastic properties of the system.