Work

Acoustic field visualization and source localization via physics-informed learning of sparse data with adaptive sampling

Year: 2024

Type: article

Abstract: 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.

Source: AIP Advances

DOI: 10.1063/5.0227921

Authors: Jian Chen, Dan Xu, Weijian Fang, Shiwei Wu, Haiteng Wu

Institutions: State Key Laboratory Fluid Power and Mechatronic Systems, Zhejiang University

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,350

Funders: National Natural Science Foundation of China, Key Research and Development Program of Zhejiang Province