Controlling Topological Defects in Polar Fluids via Reinforcement Learning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918104336957440 |
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| author | Singh, Abhinav Koumoutsakos, Petros |
| author_facet | Singh, Abhinav Koumoutsakos, Petros |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_19298 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Controlling Topological Defects in Polar Fluids via Reinforcement Learning Singh, Abhinav Koumoutsakos, Petros Soft Condensed Matter Artificial Intelligence Machine Learning 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. |
| title | Controlling Topological Defects in Polar Fluids via Reinforcement Learning |
| topic | Soft Condensed Matter Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2507.19298 |