3-D Steady Heat Conduction Solver via Deep Learning
Yinpeng Wang, Jianmei Zhou, Qiang Ren, Yaoyao Li, Donglin Su
Abstract
Conventional numerical heat conduction solvers are exceedingly computationally expensive and memory demanding. Recent advances in deep learning have witnessed its extensive application in computational physics field. Compared to traditional methods, deep learning framework emerges superior computational efficiency, providing a substitution for speeding up the calculation. In this paper, we propose an innovate deep learning framework to predict the 3D temperature field in a cubic region filled with random objects of various geometries and materials. The framework is capable of resolving both passive and active heat conduction problems. After being fully trained, it can achieve similar precision to the finite element method (FEM), while the calculation speed is accelerated by two orders of magnitude. Furthermore, the deep learning model has demonstrated robust generalization ability in predicting the temperature distribution of the cases with real-world objects not existing in the data set. We believe that the framework paves the way for solving complex heat conduction problems in engineering, as well as inverse problems in the future.