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 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.
作者
刘斌
王译浠
Bin Liu;Yixi Wang(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,Fudan University,Shanghai 200438,China)
基金
funding from the National Natural Science Foundation of China (Grant Nos.12035004 and 12320101004)
the Innovation Program of Shanghai Municipal Education Commission (Grant No.2023ZKZD06).