声波超材料中的机器学习模型

IF 12.2 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Current Opinion in Solid State & Materials Science Pub Date : 2023-12-19 DOI:10.1016/j.cossms.2023.101133

Chen-Xu Liu , Gui-Lan Yu , Zhanli Liu

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引用次数: 0

摘要

机器学习为推动声波晶体和弹性超材料的发展开辟了一条新途径。为应对声波超材料领域的各种挑战，人们采用并开发了大量学习模型。在此，我们概述了应用于声波超材料的主流机器学习模型，讨论了它们的能力和局限性，并探讨了未来研究的潜在方向。

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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Machine learning models in phononic metamaterials

Machine learning opens up a new avenue for advancing the development of phononic crystals and elastic metamaterials. Numerous learning models have been employed and developed to address various challenges in the field of phononic metamaterials. Here, we provide an overview of mainstream machine learning models applied to phononic metamaterials, discuss their capabilities as well as limitations, and explore potential directions for future research.

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来源期刊

Current Opinion in Solid State & Materials Science 工程技术-材料科学：综合

CiteScore

21.10

自引率

3.60%

发文量

审稿时长

47 days

期刊介绍： Title: Current Opinion in Solid State & Materials Science Journal Overview: Aims to provide a snapshot of the latest research and advances in materials science Publishes six issues per year, each containing reviews covering exciting and developing areas of materials science Each issue comprises 2-3 sections of reviews commissioned by international researchers who are experts in their fields Provides materials scientists with the opportunity to stay informed about current developments in their own and related areas of research Promotes cross-fertilization of ideas across an increasingly interdisciplinary field