In recent years, a significant amount of attention has been paid to solve partial differential equations (PDEs) by deep learning. For example, deep Galerkin method (DGM) uses the PDE residual in the least-squares sense as the loss function and a deep neural network (DNN) to approximate the PDE solution. In this work, we propose a deep mixed residual method (MIM) to solve PDEs with high-order derivatives. In MIM, we first rewrite a high-order PDE into a first-order system, very much in the same spirit as local discontinuous Galerkin method and mixed finite element method in classical numerical methods for PDEs. We then use the residual of first-order system in the least-squares sense as the loss function, which is in close connection with least-squares finite element method. For aforementioned classical numerical methods, the choice of trail and test functions is important for stability and accuracy issues in many cases. MIM shares this property when DNNs are employed to approximate unknowns functions in the first-order system. In one case, we use nearly the same DNN to approximate all unknown functions and in the other case, we use totally different DNNs for different unknown functions. In most cases, MIM provides better approximations (not only for high-derivatives of the PDE solution but also for the PDE solution itself) than DGM with nearly the same DNN and the same execution time, sometimes by more than one order of magnitude. When different DNNs are used, in many cases, MIM provides even better approximations than MIM with only one DNN, sometimes by more than one order of magnitude. Therefore, we expect MIM to open up a possibly systematic way to understand and improve deep learning for solving PDEs from the perspective of classical numerical analysis.
Robust residual-based a posteriori error estimators for mixed finite element methods for fourth order elliptic singularly perturbed problems
Chongqing Jiaotong University（重庆交通大学）；Texas A&M International University (TAMIU)（德克萨斯A&M国际大学）；Beijing Computational Science Research Center（北京计算科学研究中心）；Wayne State University（韦恩州立大学）
Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Hunan Key Laboratory for Computation and Simulation in Science and Engineering, School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）
A decoupled energy-stable mixed finite element method for Poisson-Nernst-Planck-Navier-Stokes equations
School of Mathematics and Computational Science, Guilin University of Electronic Technology（桂林电子科技大学数学与 computational 科学学院）；Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation（广西高校数据分析与计算重点实验室）；Guangxi Applied Mathematics Center (GUET)（广西应用数学中心（桂林电子科技大学））
