Physics-informed neural networks for solving time-dependent mode-resolved phonon Boltzmann transport equation

作     者：Jiahang Zhou Ruiyang Li Tengfei Luo 

作者机构：Department of Aerospace and Mechanical EngineeringUniversity of Notre DameNotre DameIN 46556USA Department of Chemical and Biomolecular EngineeringUniversity of Notre DameNotre DameIN 46556USA Center for Sustainable Energy at Notre Dame(ND Energy)University of Notre DameNotre DameIN 46556USA 

出 版 物：《npj Computational Materials》 (计算材料学（英文）)

年 卷 期：2023年第9卷第1期

页      面：181-189页

核心收录：

学科分类：07[理学] 082403[工学-水声工程] 08[工学] 070206[理学-声学] 0824[工学-船舶与海洋工程] 0701[理学-数学] 070101[理学-基础数学] 0702[理学-物理学] 

基　　金：The authors would like to thank ONR MURI(N00014-18-1-2429)and DARPA(HR00112390112)for the financial support The simulations are supported by the Notre Dame Center for Research Computing,and NSF through the eXtreme Science and Engineering Discovery Environment(XSEDE)computing resources provided by Texas Advanced Computing Center(TACC)Stampede II under grant number TG-CTS100078 

主　　题：phonon resolved equation 

摘      要：The phonon Boltzmann transport equation(BTE)is a powerful tool for modeling and understanding micro-/nanoscale thermal transport in solids,where Fourier’s law can fail due to non-diffusive effect when the characteristic length/time is comparable to the phonon mean free path/relaxation ***,numerically solving phonon BTE can be computationally costly due to its high dimensionality,especially when considering mode-resolved phonon properties and time *** this work,we demonstrate the effectiveness of physics-informed neural networks(PINNs)in solving time-dependent mode-resolved phonon *** PINNs are trained by minimizing the residual of the governing equations,and boundary/initial conditions to predict phonon energy distributions,without the need for any labeled training *** results obtained using the PINN framework demonstrate excellent agreement with analytical and numerical ***,after offline training,the PINNs can be utilized for online evaluation of transient heat conduction,providing instantaneous results,such as temperature *** is worth noting that the training can be carried out in a parametric setting,allowing the trained model to predict phonon transport in arbitrary values in the parameter space,such as the characteristic *** efficient and accurate method makes it a promising tool for practical applications such as the thermal management design of microelectronics.