Position control of an acoustic cavitation bubble by reinforcement learning

Reinforcement Learning (RL) is employed to develop control techniques for manipulating acoustic cavitation bubbles. This paper presents a proof of concept in which an RL agent is trained to discover a policy that allows precise control of bubble positions within a dual-frequency standing acoustic wa...

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Vydané v:	Ultrasonics sonochemistry Ročník 115; s. 107290
Hlavní autori:	Klapcsik, Kálmán, Gyires-Tóth, Bálint, Rosselló, Juan Manuel, Hegedűs, Ferenc
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier B.V 01.04.2025 Elsevier
Predmet:	Bubble dynamics Bubble position control GPU programming Reinforcement learning Selected Papers from EES 2024 Bubble dynamics GPU programming Bubble position control Reinforcement learning
ISSN:	1350-4177, 1873-2828, 1873-2828
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