Controlling Topological Defects in Polar Fluids via Reinforcement Learning

TL;DR

This paper demonstrates how reinforcement learning can be used to control topological defects in active polar fluids by modulating activity profiles, enabling precise defect manipulation and transport in complex non-equilibrium systems.

Contribution

It introduces a reinforcement learning approach to discover control strategies for defect manipulation in active matter, combining AI with hydrodynamic modeling.

Findings

Reinforcement learning effectively guides defect trajectories.

Localized control of active stress induces desired flow fields.

AI learns underlying defect dynamics for robust control.

Abstract

Topological defects in active polar fluids exhibit complex dynamics driven by internally generated stresses, reflecting the deep interplay between topology, flow, and non-equilibrium hydrodynamics. Feedback control offers a powerful means to guide such systems, enabling transitions between dynamic states. We investigated closed-loop steering of integer-charged defects in a confined active fluid by modulating the spatial profile of activity. Using a continuum hydrodynamic model, we show that localized control of active stress induces flow fields that can reposition and direct defects along prescribed trajectories by exploiting non-linear couplings in the system. A reinforcement learning framework is used to discover effective control strategies that produce robust defect transport across both trained and novel trajectories. The results highlight how AI agents can learn the underlying dynamics and spatially structure activity to manipulate topological excitations, offering insights into the controllability of active matter and the design of adaptive, self-organized materials.