Chengxi Zhong, Qingyi Lu, Teng Li, Hu Su, Song Liu
{"title":"利用物理强化对比学习进行声场重建的实时声全息技术","authors":"Chengxi Zhong, Qingyi Lu, Teng Li, Hu Su, Song Liu","doi":"10.1063/5.0174978","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":15088,"journal":{"name":"Journal of Applied Physics","volume":"53 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction\",\"authors\":\"Chengxi Zhong, Qingyi Lu, Teng Li, Hu Su, Song Liu\",\"doi\":\"10.1063/5.0174978\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":15088,\"journal\":{\"name\":\"Journal of Applied Physics\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-01-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Applied Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0174978\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1063/5.0174978","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction
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.
期刊介绍:
The Journal of Applied Physics (JAP) is an influential international journal publishing significant new experimental and theoretical results of applied physics research.
Topics covered in JAP are diverse and reflect the most current applied physics research, including:
Dielectrics, ferroelectrics, and multiferroics-
Electrical discharges, plasmas, and plasma-surface interactions-
Emerging, interdisciplinary, and other fields of applied physics-
Magnetism, spintronics, and superconductivity-
Organic-Inorganic systems, including organic electronics-
Photonics, plasmonics, photovoltaics, lasers, optical materials, and phenomena-
Physics of devices and sensors-
Physics of materials, including electrical, thermal, mechanical and other properties-
Physics of matter under extreme conditions-
Physics of nanoscale and low-dimensional systems, including atomic and quantum phenomena-
Physics of semiconductors-
Soft matter, fluids, and biophysics-
Thin films, interfaces, and surfaces