Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks
Learning the dynamics of metamaterials from diffracted waves with convolutional neural networks
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DOI：
10.1038/s43246-022-00276-w
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发表时间：
2022-08
影响因子：
7.8
通讯作者：
Yuxin Zhai;Hyung-Suk Kwon;Yunseok Choi;Dylan A. Kovacevich;B. Popa
中科院分区：
文献类型：
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作者：
Yuxin Zhai;Hyung-Suk Kwon;Yunseok Choi;Dylan A. Kovacevich;B. Popa
用于从散射机械波中识别未知介质的动力学特性的传统方法依赖于波动方程的分析或数值处理。这些方法在分析介质尺寸适中且材料边缘的衍射对散射场影响显着的情况下显示出它们的局限性，例如无损诊断和超材料表征。在这里，我们证明卷积神经网络可以解释衍射场，并从一小组数值模拟中学习散射场与所有有效材料参数（包括质量密度和刚度张量）之间的映射。此外，使用合成数据训练的网络可以处理物理测量，并且对测量误差非常鲁棒。更重要的是，经过训练的网络可以深入了解物质的动态行为，包括对每种材料特性的散射场敏感性的定量测量，以及敏感性如何根据被测材料而变化。
Conventional methods used to identify the dynamical properties of unknown media from scattered mechanical waves rely on analytical or numerical manipulations of the wave equation. These methods show their limitations in scenarios where the analyzed medium is moderately sized and the diffraction from the material edges influences the scattered fields significantly, such as non-destructive diagnostics and metamaterial characterization. Here, we show that convolutional neural networks can interpret the diffracted fields and learn the mapping between the scattered fields and all the effective material parameters including mass density and stiffness tensors from a small set of numerical simulations. Furthermore, networks trained with synthetic data can process physical measurements and are very robust to measurement errors. More importantly, the trained network provides insight into the dynamic behavior of matter including quantitative measures of the scattered field sensitivity to each material property and how the sensitivity changes depending on the material under test.