Controlling Topological Defects in Polar Fluids via Reinforcement Learning

2025-07-25

AI Summary

This study addresses the challenge of precise manipulation of topological defects in polar active fluids. We propose a closed-loop feedback control framework based on deep reinforcement learning (DRL) to achieve stable, trajectory-guided transport of integer-charge topological defects. By locally modulating spatially resolved active stress, our method induces nonequilibrium flow fields that drive defect motion along arbitrary user-specified paths. To our knowledge, this is the first application of DRL to dynamic topological defect control in active fluids. Integrating a continuum hydrodynamic model with real-time state feedback, the agent achieves robust path tracking and sub-defect-scale positioning—even on previously unseen trajectories. Results demonstrate that the AI agent autonomously learns strongly nonlinear, far-from-equilibrium fluid dynamics and exploits intrinsic system couplings for adaptive control. This work establishes a new paradigm for designing intelligent, responsive soft matter and self-organizing functional materials.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Reinforcement Learning
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Control topological defects in active polar fluids
• Steer defects using reinforcement learning strategies
• Manipulate active matter dynamics via spatial activity modulation

Innovation

• Reinforcement learning controls topological defects
• Modulating activity profiles steers defect dynamics
• AI agents learn non-linear hydrodynamic couplings