利用深度学习预测复杂网络的热传导率

IF 3.5 2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY
Changliang Zhu, Xiangying Shen, Guimei Zhu, Baowen Li
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引用次数: 0

摘要

在材料科学与工程领域,预测复杂网络的热导率是一项艰巨的挑战。这一挑战是由于网络结构参数与热导率之间错综复杂的相互作用造成的,其中包括连通性、网络拓扑结构、网络几何形状、节点不均匀性等。我们对这些参数如何具体影响传热性能的了解仍然有限。深度学习为解决此类复杂问题提供了一种前景广阔的方法。我们发现,成熟的卷积神经网络模型 AlexNet 可以高效预测复杂网络的热传导率。考虑到热传导本质上是由拉普拉卡矩阵编码的,我们的方法通过减少图像识别进一步优化了计算效率。有趣的是,我们的研究结果表明,采用更简单的卷积神经网络架构可以达到相当的预测精度,同时所需的计算时间更短。这一结果有助于为预测复杂网络的热导提供更有效的解决方案,并为相关领域的机器学习算法提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Thermal Conductance of Complex Networks with Deep Learning
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.
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来源期刊
Chinese Physics Letters
Chinese Physics Letters 物理-物理:综合
CiteScore
5.90
自引率
8.60%
发文量
13238
审稿时长
4 months
期刊介绍: Chinese Physics Letters provides rapid publication of short reports and important research in all fields of physics and is published by the Chinese Physical Society and hosted online by IOP Publishing.
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