Swimming in Turbulent Environments with Physics Informed Reinforcement Learning

Turbulent diffusion drives the separation of particles initially in close proximity. Understanding

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.