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
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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 | |
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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条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | 操作 | |
Prediction of Therma(2361KB) | -- | -- | 限制开放 | -- |
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