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

doi:10.1063/5.0174978

	Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction
	Zhong, Chengxi 1; Lu, Qingyi 1; Li, Teng 1; Su, Hu2 ; Liu, Song 1,3
发表期刊	JOURNAL OF APPLIED PHYSICS
ISSN	0021-8979
	2024-01-07
卷号	135 期号:1 页码:12
通讯作者	Su, Hu(hu.su@ia.ac.cn) ; Liu, Song(liusong@shanghaitech.edu.cn)
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.
DOI	10.1063/5.0174978
关键词[WOS]	PHASE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China10.13039/501100001809
项目资助者	National Natural Science Foundation of China10.13039/501100001809
WOS研究方向	Physics
WOS类目	Physics, Applied
WOS记录号	WOS:001206623100006
出版者	AIP Publishing
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/57003
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
通讯作者单位	中国科学院自动化研究所
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.