Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session A30: Reinforcement Learning for Flow Control
8:00 AM–9:57 AM,
Sunday, November 19, 2023
Room: 154AB
Chair: Ricardo Vinuesa, KTH (Royal Institute of Technology)
Abstract: A30.00002 : Swimming in Turbulent Environments with Physics Informed Reinforcement Learning
8:13 AM–8:26 AM
Presenter:
Christopher F Koh
(University of Arizona)
Authors:
Christopher F Koh
(University of Arizona)
Michael Chertkov
(University of Arizona)
Laurent Pagnier
(University of Arizona)
the energy expenditure required to maintain sufficient closeness between an actively swimming
particle and its passively advected partner is crucial. In this study, we address this fundamental
inquiry by investigating three distinct strategies: physics-uninformed, semi-informed, and informed
Reinforcement Learning. Specifically, we examine the scenario of an active particle swimming amidst
a large-scale turbulent flow, striving to keep in sight its passive counterpart. By analyzing these
strategies, we aim to determine the most effective and efficient means of balancing swimming efforts
with the goal of proximity maintenance in turbulent environments. Our findings shed light on the
optimal tactics to enhance the cohesion between actively driven and passively transported particles
in turbulent flows.
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