Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling
Traditional methods for acoustic field visualization require considerable effort for capturing large amounts of acoustic data to achieve a high resolution field map, highly limiting their widespread use. In this study, we propose an approach for acoustic field visualization based on physics-informed...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2024-11-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0227921 |
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| author | Jian Chen Dan Xu Weijian Fang Shiwei Wu Haiteng Wu |
| author_facet | Jian Chen Dan Xu Weijian Fang Shiwei Wu Haiteng Wu |
| author_sort | Jian Chen |
| collection | DOAJ |
| description | Traditional methods for acoustic field visualization require considerable effort for capturing large amounts of acoustic data to achieve a high resolution field map, highly limiting their widespread use. In this study, we propose an approach for acoustic field visualization based on physics-informed neural networks (PINNs) by using a small amount of data, subsequently realizing accurate acoustic source localization. First, we present a PINN model integrated with an acoustic Helmholtz equation and adaptive sampling, the performance of which is testified via numerical simulations. The “no mesh” character of PINN enables achieving high resolution acoustic field visualization without requiring the capture of numerous data in advance. Furthermore, we experimentally validate the performance of the proposed method, which demonstrates that the acoustic sources can be precisely localized with sparse field data acquisition within a small area. This work would find potential applications in various acoustics, such as acoustic communication, biomedical imaging, and virtual reality. |
| format | Article |
| id | doaj-art-5e92f712ddef48568e4efb71b1ea4e84 |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-5e92f712ddef48568e4efb71b1ea4e842025-08-20T02:30:46ZengAIP Publishing LLCAIP Advances2158-32262024-11-011411115308115308-610.1063/5.0227921Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive samplingJian Chen0Dan Xu1Weijian Fang2Shiwei Wu3Haiteng Wu4State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, ChinaKey Laboratory of Intelligent Robot for Operation and Maintenance of Zhejiang Province, Hangzhou Shenhao Technology, Hangzhou 311121, ChinaTraditional methods for acoustic field visualization require considerable effort for capturing large amounts of acoustic data to achieve a high resolution field map, highly limiting their widespread use. In this study, we propose an approach for acoustic field visualization based on physics-informed neural networks (PINNs) by using a small amount of data, subsequently realizing accurate acoustic source localization. First, we present a PINN model integrated with an acoustic Helmholtz equation and adaptive sampling, the performance of which is testified via numerical simulations. The “no mesh” character of PINN enables achieving high resolution acoustic field visualization without requiring the capture of numerous data in advance. Furthermore, we experimentally validate the performance of the proposed method, which demonstrates that the acoustic sources can be precisely localized with sparse field data acquisition within a small area. This work would find potential applications in various acoustics, such as acoustic communication, biomedical imaging, and virtual reality.http://dx.doi.org/10.1063/5.0227921 |
| spellingShingle | Jian Chen Dan Xu Weijian Fang Shiwei Wu Haiteng Wu Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling AIP Advances |
| title | Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling |
| title_full | Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling |
| title_fullStr | Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling |
| title_full_unstemmed | Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling |
| title_short | Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling |
| title_sort | acoustic field visualization and source localization via physics informed learning of sparse data with adaptive sampling |
| url | http://dx.doi.org/10.1063/5.0227921 |
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