Application Progress of machine learning in acoustic metamaterials

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (23) : 280-293.

ZHANG Benben, MIAO Linchang, ZHENG Haizhong, XIAO Peng

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Abstract

Acoustic metamaterials, as a new category of artificial composite structural materials, possess many innovative and anomalous physical properties that are not available in natural materials, which provide a brand-new research pathway and application opportunity for the effective control and precise regulation of acoustic waves. However, in order to obtain the specific structure-function response of acoustic metamaterials, the traditional design methods need to continuously and repeatedly adjust the material parameters or structural morphology during theoretical derivation, numerical simulation, and experimental validation, which substantially increases the research computational cost. Machine learning, with powerful nonlinear fitting capabilities, can bypass the physical modelling process through optimization algorithms and directly construct appropriate mapping relationships in the parameter space to meet the target function, providing the possibility of breaking through the high limitations of traditional physical design strategies. This paper reviews advances in the application of machine learning to acoustic metamaterials over the last few years. Firstly, the basic development of acoustic metamaterials and the fundamentals of mainstream machine learning algorithms are briefly outlined, and then the latest research results on the application of machine learning in phononic crystals, acoustic metamaterials, and acoustic metamaterials topology design are presented in detail, and finally, the current research status in the field is discussed and outlooked accordingly.

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ZHANG Benben, MIAO Linchang, ZHENG Haizhong, XIAO Peng. Application Progress of machine learning in acoustic metamaterials[J]. Journal of Vibration and Shock, 2024, 43(23): 280-293

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