We consider a space-time variational formulation of parabolic initial-boundary value problems in anisotropic Sobolev spaces in combination with a Hilbert-type transformation. This variational setting is the starting point for the space-time Galerkin finite element discretization that leads to a large global linear system of algebraic equations. We propose and investigate new efficient direct solvers for this system. In particular, we use a tensor-product approach with piecewise polynomial, globally continuous ansatz and test functions. The developed solvers are based on the Bartels-Stewart method and on the Fast Diagonalization method, which result in solving a sequence of spatial subproblems. The solver based on the Fast Diagonalization method allows to solve these spatial subproblems in parallel leading to a full parallelization in time. We analyze the complexity of the proposed algorithms, and give numerical examples for a two-dimensional spatial domain, where sparse direct solvers for the spatial subproblems are used.
SSBE-PINN: A Sobolev Boundary Scheme Boosting Stability and Accuracy in Elliptic/Parabolic PDE Learning
Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（港科大深圳-香港联合创新研究院）；University of Michigan（密歇根大学）
Fast multilevel solvers for a class of discrete fourth order parabolic problems
Pacific Northwest National Laboratory（太平洋西北国家实验室）；Southwest Jiaotong University（西南交通大学）；Tufts University（塔夫茨大学）；University of California, Irvine（加州大学尔湾分校）；University of Maryland（马里兰大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Time-parallel iterative solvers for parabolic evolution equations
Institute of Computational Mathematics, Johannes Kepler University Linz（林茨约翰·开普勒大学计算数学研究所）；Department of Mathematics, University College London（伦敦大学学院数学系）
Stochastic Galerkin and Monte-Carlo methods for parabolic problems: Numerical performance of variational matrix-free approximations
Helmut Schmidt University, University of the Federal Armed Forces Hamburg（赫尔穆特·施密特大学，德国联邦国防军汉堡大学）；University of Magdeburg, Institute for Analysis and Numerics（马格德堡大学，分析与数值研究所）
Error analysis of mixed finite element methods for nonlinear parabolic equations
School of Mathematics and Statistics, Huazhong University of Science and Technology（华中科技大学数学与统计学院）；Department of Mathematics, City University of Hong Kong（香港城市大学数学系）
A Structure-Preserving Framework for Solving Parabolic Partial Differential Equations with Neural Networks
Department of Applied Mathematics, The Hong Kong Polytechnic University（应用数学系，香港Polytechnic大学）；Department of Mathematics, University of South Carolina（数学系，南卡罗来纳大学）
