Prediction of Thermal Conductance of Complex Networks with Deep Learning

Abstract

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.