Application of machine learning on the design of acoustic metamaterials and phonon crystals: a review

IF 3.7 3区材料科学 Q1 INSTRUMENTS & INSTRUMENTATION

Smart Materials and Structures Pub Date : 2024-06-06 DOI:10.1088/1361-665x/ad51bc

Jianquan Chen, Jiahan Huang, Mingyi An, Pengfei Hu, Yiyuan Xie, Junjun Wu, Yu Chen

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引用次数: 0

Abstract

This comprehensive review explores the design and applications of machine learning (ML) techniques to acoustic metamaterials (AMs) and phononic crystals (PnCs), with a particular focus on deep learning (DL). AMs and PnCs, characterized by artificially designed microstructures and geometries, offer unique acoustic properties for precise control and manipulation of sound waves. ML, including DL, in combination with traditional artificial design have promoted the design process, enabling data-driven approaches for feature identification, design optimization, and intelligent parameter search. ML algorithms process extensive AM data to discover novel structures and properties, enhancing overall acoustic performance. This review presents an in-depth exploration of applications associated with ML techniques in AMs and PnCs, highlighting specific advantages, challenges and potential solutions of applying of using ML algorithms associated with ML techniques. By bridging acoustic engineering and ML, this review paves the way for future breakthroughs in acoustic research and engineering.

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机器学习在声超材料和声子晶体设计中的应用：综述

这篇综述探讨了机器学习（ML）技术在声学超材料（AMs）和声子晶体（PnCs）中的设计和应用，尤其侧重于深度学习（DL）。AMs 和 PnCs 以人工设计的微结构和几何形状为特征，具有独特的声学特性，可精确控制和操纵声波。包括 DL 在内的 ML 与传统的人工设计相结合，促进了设计过程，使数据驱动的特征识别、设计优化和智能参数搜索方法成为可能。ML 算法通过处理大量 AM 数据来发现新的结构和特性，从而提高整体声学性能。本综述深入探讨了 AM 和 PnC 中与 ML 技术相关的应用，重点介绍了应用与 ML 技术相关的 ML 算法的具体优势、挑战和潜在解决方案。通过将声学工程与 ML 联系起来，本综述为声学研究和工程领域的未来突破铺平了道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Smart Materials and Structures 工程技术-材料科学：综合

CiteScore

7.50

自引率

12.20%

发文量

317

审稿时长

3 months

期刊介绍： Smart Materials and Structures (SMS) is a multi-disciplinary engineering journal that explores the creation and utilization of novel forms of transduction. It is a leading journal in the area of smart materials and structures, publishing the most important results from different regions of the world, largely from Asia, Europe and North America. The results may be as disparate as the development of new materials and active composite systems, derived using theoretical predictions to complex structural systems, which generate new capabilities by incorporating enabling new smart material transducers. The theoretical predictions are usually accompanied with experimental verification, characterizing the performance of new structures and devices. These systems are examined from the nanoscale to the macroscopic. SMS has a Board of Associate Editors who are specialists in a multitude of areas, ensuring that reviews are fast, fair and performed by experts in all sub-disciplines of smart materials, systems and structures. A smart material is defined as any material that is capable of being controlled such that its response and properties change under a stimulus. A smart structure or system is capable of reacting to stimuli or the environment in a prescribed manner. SMS is committed to understanding, expanding and dissemination of knowledge in this subject matter.