Optimal tracking strategies in a turbulent flow
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Abstract (arXiv)
Pursuing a drifting target in a turbulent flow is an extremely difficult task whenever the searcher has limited propulsion and maneuvering capabilities. Even in the case when the relative distance between pursuer and target stays below the turbulent dissipative scale, the chaotic nature of the trajectory of the target represents a formidable challenge. Here, we show how to successfully apply optimal control theory to find navigation strategies that overcome chaotic dispersion and allow the searcher to reach the target in a minimal time. We contrast the results of optimal control -- which requires perfect observability and full knowledge of the dynamics of the environment -- with heuristic algorithms that are reactive -- relying on local, instantaneous information about the flow. While the latter display significantly worse performances, optimally controlled pursuers can track the target for times much longer than the typical inverse Lyapunov exponent and are considerably more robust.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 2.
5 papers.
- 2023Synthetic Lagrangian Turbulence by Generative Diffusion Modelscited by 31
- 2024Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulencecited by 2
- 2024Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Modelscited by 1
- 2025A critical assessment of reinforcement learning methods for microswimmer navigation in complex flowscited by 0
- 2025Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flowscited by 0
- 2025Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows21 Jul 2025
- 2025A critical assessment of reinforcement learning methods for microswimmer navigation in complex flows8 May 2025
- 2024Stochastic Reconstruction of Gappy Lagrangian Turbulent Signals by Conditional Diffusion Models31 Oct 2024
- 2024Physics-Guided Actor-Critic Reinforcement Learning for Swimming in Turbulence5 Jun 2024
- 2023Synthetic Lagrangian Turbulence by Generative Diffusion Models17 Jul 2023