Metamaterial characterization from far-field acoustic wave measurements using convolutional neural network
Metamaterial characterization from far-field acoustic wave measurements using convolutional neural network
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DOI：
10.3389/fphy.2022.1021887
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Yeonjoon Cheong;Hyung-Suk Kwon;B. Popa
中科院分区：
文献类型：
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作者：
Yeonjoon Cheong;Hyung-Suk Kwon;B. Popa
在体内组织健康诊断和超材料表征等领域，识别未知介质的材料特性是一项重要的科学/工程挑战。目前，已有利用解析或数值方法从自由空间中的弹性波散射中提取大型未知介质的材料参数的技术。然而，将这些方法应用于直径几个波长数量级的小样本是具有挑战性的，因为这些样本的散射场受到来自样本边缘的衍射的严重污染。这里，我们提出了一种利用卷积神经网络来提取小样本材料参数的方法，该网络被训练成学习远场回波与材料参数之间的映射。用模拟水下未知介质声的自由空间散射得到的合成时域回波数据来训练网络。结果表明，即使使用较小的训练集，神经网络也可以准确地预测有效的材料参数，如质量密度、体积弹性模量和剪切弹性模量。此外，我们在水箱中进行的实验表明，用合成数据训练的网络可以从远场执行的单点回波测量中准确地估计制作的超材料样品的材料特性。这项工作突出了我们利用衍射场主导的远场声反射识别未知介质的有效性，并将为声传感技术开辟一条新的途径。
Identifying the material properties of unknown media is an important scientific/engineering challenge in areas as varied as in-vivo tissue health diagnostics and metamaterial characterization. Currently, techniques exist to retrieve the material parameters of large unknown media from elastic wave scattering in free-space using analytical or numerical methods. However, applying these methods to small samples on the order of few wavelengths in diameter is challenging, as the fields scattered by these samples become significantly contaminated by diffraction from the sample edges. Here, we propose a method to extract the material parameters of small samples using convolutional neural networks trained to learn the mapping between far-field echoes and the material parameters. Networks were trained with synthetic time domain echo data obtained by simulating the free-space scattering of sound from unknown media underwater. Results show that neural networks can accurately predict effective material parameters such as mass density, bulk modulus, and shear modulus even when small training sets are used. Furthermore, we demonstrate in experiments executed in a water tank that the networks trained with synthetic data can accurately estimate the material properties of fabricated metamaterial samples from single-point echo measurements performed in the far-field. This work highlights the effectiveness of our approach to identify unknown media using far-field acoustic reflection dominated by diffraction fields and will open a new avenue toward acoustic sensing techniques.