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Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction

文献类型：期刊论文

作者Zhong, Chengxi1; Lu, Qingyi1; Li, Teng1; Su, Hu2; Liu, Song1,3
刊名JOURNAL OF APPLIED PHYSICS
出版日期2024-01-07
卷号135期号:1页码:12
ISSN号0021-8979
DOI10.1063/5.0174978
通讯作者Su, Hu(hu.su@ia.ac.cn) ; Liu, Song(liusong@shanghaitech.edu.cn)
英文摘要Acoustic holography (AH) provides a promising technique for arbitrary acoustic field reconstruction, supporting many applications like robotic micro-nano manipulation, neuromodulation, volumetric imaging, and virtual reality. In AH, three-dimensional (3D) acoustic fields quantified with complex-valued acoustic pressures are reconstructed by virtue of two-dimensional (2D) acoustic holograms. Phase-only hologram (POH) is recently regarded as an energy-efficient way for AH, which is typically implemented by a dynamically programmable phased array of transducers (PATs). As a result, spatiotemporal precise acoustic field reconstruction is enabled by precise, dynamic, and individual actuation of PAT. Thus, 2D POH is required per arbitrary acoustic fields, which can be viewed as a physical inverse problem. However, solving the aforementioned physical inverse problem in numerical manners poses challenges due to its non-linear, high-dimensional, and complex coupling natures. The existing iterative algorithms like the iterative angular spectrum approach (IASA) and iterative backpropagation (IB) still suffer from speed-accuracy trade-offs. Hence, this paper explores a novel physics-iterative-reinforced deep learning method, in which frequency-argument contrastive learning is proposed facilitated by the inherent physical nature of AH, and the energy conservation law is under consideration. The experimental results demonstrate the effectiveness of the proposed method for acoustic field reconstruction, highlighting its significant potential in the domain of acoustics, and pushing forward the combination of physics into deep learning.
WOS关键词PHASE
资助项目National Natural Science Foundation of China10.13039/501100001809
WOS研究方向Physics
语种英语
WOS记录号WOS:001206623100006
出版者AIP Publishing
资助机构National Natural Science Foundation of China10.13039/501100001809
源URL[http://ir.ia.ac.cn/handle/173211/57002]  
专题多模态人工智能系统全国重点实验室
通讯作者Su, Hu; Liu, Song
作者单位1.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
3.Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China
推荐引用方式
GB/T 7714		 Zhong, Chengxi,Lu, Qingyi,Li, Teng,et al. Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction[J]. JOURNAL OF APPLIED PHYSICS,2024,135(1):12.
APA Zhong, Chengxi,Lu, Qingyi,Li, Teng,Su, Hu,&Liu, Song.(2024).Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction.JOURNAL OF APPLIED PHYSICS,135(1),12.
MLA Zhong, Chengxi,et al."Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction".JOURNAL OF APPLIED PHYSICS 135.1(2024):12.

入库方式： OAI收割

来源：自动化研究所

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