中文版 | English
题名

Prediction of Thermal Conductance of Complex Networks with Deep Learning

作者
通讯作者Shen, Xiangying; Zhu, Guimei
发表日期
2023-11-01
DOI
发表期刊
ISSN
0256-307X
EISSN
1741-3540
卷号40期号:12
摘要

Predicting thermal conductance of complex networks poses a formidable challenge in the field of materials science and engineering. This challenge arises due to the intricate interplay between the parameters of network structure and thermal conductance, encompassing connectivity, network topology, network geometry, node inhomogeneity, and others. Our understanding of how these parameters specifically influence heat transfer performance remains limited. Deep learning offers a promising approach for addressing such complex problems. We find that the well-established convolutional neural network models AlexNet can predict the thermal conductance of complex network efficiently. Our approach further optimizes the calculation efficiency by reducing the image recognition in consideration that the thermal transfer is inherently encoded within the Laplacian matrix. Intriguingly, our findings reveal that adopting a simpler convolutional neural network architecture can achieve a comparable prediction accuracy while requiring less computational time. This result facilitates a more efficient solution for predicting the thermal conductance of complex networks and serves as a reference for machine learning algorithm in related domains.

相关链接[来源记录]
收录类别
SCI ; EI
语种
英语
学校署名
第一 ; 通讯
资助项目
National Natural Science Foundation of China[
WOS研究方向
Physics
WOS类目
Physics, Multidisciplinary
WOS记录号
WOS:001114088500001
出版者
EI入藏号
20234915160835
EI主题词
Convolution ; Convolutional neural networks ; Deep learning ; Forecasting ; Heat transfer ; Image recognition ; Learning algorithms ; Learning systems ; Matrix algebra ; Network architecture ; Thermal conductivity
EI分类号
Ergonomics and Human Factors Engineering:461.4 ; Thermodynamics:641.1 ; Heat Transfer:641.2 ; Information Theory and Signal Processing:716.1 ; Computer Systems and Equipment:722 ; Machine Learning:723.4.2 ; Algebra:921.1
ESI学科分类
PHYSICS
来源库
Web of Science
引用统计
被引频次[WOS]:4
成果类型期刊论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/638901
专题工学院_材料科学与工程系
理学院_物理系
工学院_深港微电子学院
作者单位
1.Southern Univ Sci & Technol, Dept Mat Sci & Engn, Shenzhen 518055, Peoples R China
2.Southern Univ Sci & Technol, Sch Microelect, Shenzhen 518055, Peoples R China
3.Southern Univ Sci & Technol, Dept Phys, Shenzhen 518055, Peoples R China
4.Shenzhen Int Quantum Acad, Shenzhen 518017, Peoples R China
第一作者单位材料科学与工程系
通讯作者单位材料科学与工程系;  深港微电子学院
第一作者的第一单位材料科学与工程系
推荐引用方式
GB/T 7714
Zhu, Changliang,Shen, Xiangying,Zhu, Guimei,et al. Prediction of Thermal Conductance of Complex Networks with Deep Learning[J]. CHINESE PHYSICS LETTERS,2023,40(12).
APA
Zhu, Changliang,Shen, Xiangying,Zhu, Guimei,&Li, Baowen.(2023).Prediction of Thermal Conductance of Complex Networks with Deep Learning.CHINESE PHYSICS LETTERS,40(12).
MLA
Zhu, Changliang,et al."Prediction of Thermal Conductance of Complex Networks with Deep Learning".CHINESE PHYSICS LETTERS 40.12(2023).
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