用强化学习控制活性流体中的拓扑缺陷移动路径
Controlling Topological Defects in Polar Fluids via Reinforcement Learning
- 通过强化学习优化活性应力分布，实现缺陷精准定位
- 可稳定引导缺陷沿预设轨迹移动，跨不同路径仍有效
- 适合研究活性物质调控与智能软材料设计的读者
活性极性流体中的拓扑缺陷表现出由内部应力驱动的复杂动力学，体现了拓扑、流动与非平衡流体力学的深层耦合。反馈控制为引导此类系统状态转换提供了有效手段。本文通过调节活性空间分布，在受限体系中实现了对整数电荷缺陷的闭环调控。基于连续流体动力学模型，我们发现局域化控制活性应力可激发特定流场，利用系统非线性耦合实现缺陷的重定位与定向运动。采用强化学习框架，成功发现能在训练及新轨迹上实现鲁棒缺陷传输的有效控制策略。结果表明，人工智能代理能够学习系统内在动力学，并通过空间结构化活性来操控拓扑激发，为活性物质的可控性研究及自适应自组织材料的设计提供新思路。
原文摘要 · Abstract (English)
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.
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