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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| Vydáno v: | Ultrasonics sonochemistry Ročník 115; s. 107290 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.04.2025
Elsevier |
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| ISSN: | 1350-4177, 1873-2828, 1873-2828 |
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| Abstract | 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 wave field by adjusting the pressure amplitude values. The agent is rewarded for driving the bubble to a target position in the shortest possible time. The results demonstrate that the agent exploits the nonlinear behaviour of the bubble and, in specific cases, identifies solutions that cannot be addressed using the linear theory of the primary Bjerknes force. The RL agent performs well under domain randomization, indicating that the RL approach generalizes effectively and produces models robust against noise, which could arise in real-world applications. |
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| AbstractList | 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 wave field by adjusting the pressure amplitude values. The agent is rewarded for driving the bubble to a target position in the shortest possible time. The results demonstrate that the agent exploits the nonlinear behaviour of the bubble and, in specific cases, identifies solutions that cannot be addressed using the linear theory of the primary Bjerknes force. The RL agent performs well under domain randomization, indicating that the RL approach generalizes effectively and produces models robust against noise, which could arise in real-world applications. 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 wave field by adjusting the pressure amplitude values. The agent is rewarded for driving the bubble to a target position in the shortest possible time. The results demonstrate that the agent exploits the nonlinear behaviour of the bubble and, in specific cases, identifies solutions that cannot be addressed using the linear theory of the primary Bjerknes force. The RL agent performs well under domain randomization, indicating that the RL approach generalizes effectively and produces models robust against noise, which could arise in real-world applications.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 wave field by adjusting the pressure amplitude values. The agent is rewarded for driving the bubble to a target position in the shortest possible time. The results demonstrate that the agent exploits the nonlinear behaviour of the bubble and, in specific cases, identifies solutions that cannot be addressed using the linear theory of the primary Bjerknes force. The RL agent performs well under domain randomization, indicating that the RL approach generalizes effectively and produces models robust against noise, which could arise in real-world applications. |
| ArticleNumber | 107290 |
| Author | Gyires-Tóth, Bálint Hegedűs, Ferenc Klapcsik, Kálmán Rosselló, Juan Manuel |
| Author_xml | – sequence: 1 givenname: Kálmán orcidid: 0000-0002-8928-5473 surname: Klapcsik fullname: Klapcsik, Kálmán email: kklapcsik@hds.bme.hu organization: Department of Hydrodynamic Systems, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Mű,egyetem rkp. 3., H-1111 Budapest, Hungary – sequence: 2 givenname: Bálint orcidid: 0000-0003-1059-9822 surname: Gyires-Tóth fullname: Gyires-Tóth, Bálint email: toth.b@tmit.bme.hu organization: Department of Telecommunications and Media Informatics, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Mű,egyetem rkp. 3., H-1111 Budapest, Hungary – sequence: 3 givenname: Juan Manuel orcidid: 0000-0001-7005-9452 surname: Rosselló fullname: Rosselló, Juan Manuel email: jrossello.research@gmail.com organization: Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva 6, 1000 Ljubljana, Slovenia – sequence: 4 givenname: Ferenc orcidid: 0000-0002-8693-1660 surname: Hegedűs fullname: Hegedűs, Ferenc email: fhegedus@hds.bme.hu organization: Department of Hydrodynamic Systems, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Mű,egyetem rkp. 3., H-1111 Budapest, Hungary |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40058020$$D View this record in MEDLINE/PubMed |
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| Keywords | Bubble dynamics GPU programming Bubble position control Reinforcement learning |
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| Title | Position control of an acoustic cavitation bubble by reinforcement learning |
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