A graph neural network approach to the inverse design for thermal transparency with periodic interparticle system

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外文摘要：Recent years have witnessed significant advances in utilizing machine learning-based techniques for thermal metamaterial-based structures and devices to attain favorable thermal transport behaviors.Among the various thermal transport behaviors,achieving thermal transparency stands out as particularly desirable and intriguing.Our earlier work demonstrated the use of a thermal metamaterial-based periodic interparticle system as the underlying structure for ma-nipulating thermal transport behavior and achieving thermal transparency.In this paper,we introduce an approach based on graph neural network to address the complex inverse design problem of determining the design parameters for a ther-mal metamaterial-based periodic interparticle system with the desired thermal transport behavior.Our work demonstrates that combining graph neural network modeling and inference is an effective approach for solving inverse design problems associated with attaining desirable thermal transport behaviors using thermal metamaterials.

外文关键词：

thermal metamaterialthermal transparencyinverse designmachine learninggraph neural net-work

作者：

刘斌、王译浠

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作者单位：

Department of Electronic Information and Artificial Intelligence,LeShan Normal University,LeShan 614099,China

Department of Physics,State Key Laboratory of Surface Physics,and Key Laboratory of Micro and Nano Photonic Structures(MOE),Fudan University,Shanghai 200438,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaInnovation Program of Shanghai Municipal Education Commission

项目编号：

12035004123201010042023ZKZD06

出版年：

2024

DOI：

10.1088/1674-1056/ad4326

中国物理B(英文版)

中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI

影响因子：0.995

ISSN：1674-1056

年,卷(期)：2024.33(8)