利用物理强化对比学习进行声场重建的实时声全息技术

IF 2.7 3区 物理与天体物理 Q2 PHYSICS, APPLIED Journal of Applied Physics Pub Date : 2024-01-05 DOI:10.1063/5.0174978
Chengxi Zhong, Qingyi Lu, Teng Li, Hu Su, Song Liu
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引用次数: 0

摘要

声全息(AH)为任意声场重建提供了一种前景广阔的技术,支持机器人微纳操纵、神经调控、容积成像和虚拟现实等多种应用。在声全息技术中,三维(3D)声场通过二维(2D)声全息图进行量化。相位全息图(POH)最近被认为是一种高效节能的声场重建方法,通常由动态可编程相位阵列换能器(PAT)实现。因此,通过对相控阵的精确、动态和单独驱动,可以实现时空精确声场重建。因此,任意声场都需要二维 POH,这可以看作是一个物理逆问题。然而,由于其非线性、高维和复杂耦合的特性,用数值方法解决上述物理逆问题构成了挑战。现有的迭代算法,如迭代角谱法(IASA)和迭代反向传播法(IB),仍然存在速度与精度的权衡问题。因此,本文探索了一种新颖的物理迭代-强化深度学习方法,即利用 AH 固有的物理特性,考虑能量守恒定律,提出频率-参数对比学习。实验结果证明了所提方法在声场重建中的有效性,凸显了其在声学领域的巨大潜力,并推动了物理学与深度学习的结合。
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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.
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来源期刊
Journal of Applied Physics
Journal of Applied Physics 物理-物理:应用
CiteScore
5.40
自引率
9.40%
发文量
1534
审稿时长
2.3 months
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
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