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

doi:10.1088/1674-1056/ad4326

Abstract 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 manipulating 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 thermal 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.

Keywords: thermal metamaterial thermal transparency inverse design machine learning graph neural network

Received: 23 February 2024
Revised: 08 April 2024
Accepted manuscript online:

PACS:	44.05.+e	(Analytical and numerical techniques)
	44.90.+c	(Other topics in heat transfer)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	65.90.+i	(Other topics in thermal properties of condensed matter)

Fund: We gratefully acknowledge funding from the National Natural Science Foundation of China (Grant Nos. 12035004 and 12320101004) and the Innovation Program of Shanghai Municipal Education Commission (Grant No. 2023ZKZD06).

Corresponding Authors: Bin Liu
E-mail: liubin@fudan.edu.cn

Cite this article:

Bin Liu(刘斌) and Yixi Wang(王译浠) A graph neural network approach to the inverse design for thermal transparency with periodic interparticle system 2024 Chin. Phys. B 33 084401

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

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