设计声子晶体和弹性超材料的深度学习

IF 4.8 2区工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Design and Engineering Pub Date : 2023-02-06 DOI:10.1093/jcde/qwad013

Chen-Xu Liu, Gui-Lan Yu

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引用次数: 9

摘要

以数据为载体的计算机革命为声子晶体(pnc)和弹性超材料(EMs)的设计提供了一种创新的方法。通过建立pnc /EMs的解析代理模型，基于人工神经网络(ann)的深度学习具有快速、准确的设计优势，弥补了传统设计方法的不足。本文综述了深度学习在pnc和EMs的前向预测、参数设计和拓扑设计方面的最新进展。并对这一新兴领域的挑战和前景进行了评述。

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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Deep learning for the design of phononic crystals and elastic metamaterials

The computer revolution coming by way of data provides an innovative approach for the design of phononic crystals (PnCs) and elastic metamaterials (EMs). By establishing an analytical surrogate model for PnCs/EMs, deep learning based on artificial neural networks (ANNs) possesses the superiorities of rapidity and accuracy in design, making up for the shortcomings of traditional design methods. Here, the recent progresses on deep learning for forward prediction, parameter design, and topology design of PnCs and EMs are reviewed. The challenges and perspectives in this emerging field are also commented.

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来源期刊

Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction

CiteScore

7.70

自引率

20.40%

发文量

125

期刊介绍： Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.