Dynamically orthogonal tensor methods for high-dimensional nonlinear PDEs

被引:20
作者
Dektor, Alec [1 ]
Venturi, Daniele [1 ]
机构
[1] Univ Calif Santa Cruz, Dept Appl Math, Santa Cruz, CA 95064 USA
关键词
High-dimensional PDEs; Hierarchical tensor methods; Dynamically orthogonal modes; Bi-orthogonal decomposition; PROBABILITY DENSITY-FUNCTION; POLYNOMIAL INTERPOLATION; BIORTHOGONAL METHOD; NUMERICAL-METHODS; INTEGRATION; ALGORITHMS; DECOMPOSITION; OPTIMIZATION; NETWORKS; INVERSE;
D O I
10.1016/j.jcp.2019.109125
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
We develop new dynamically orthogonal tensor methods to approximate multivariate functions and the solution of high-dimensional time-dependent nonlinear partial differential equations (PDEs). The key idea relies on a hierarchical decomposition of the approximation space obtained by splitting the independent variables of the problem into disjoint subsets. This process, which can be conveniently visualized in terms of binary trees, yields series expansions analogous to the classical Tensor-Train and Hierarchical Tucker tensor formats. By enforcing dynamic orthogonality conditions at each level of the binary tree, we obtain coupled evolution equations for the modes spanning each subspace within the hierarchical decomposition. This allows us to effectively compute the solution to high-dimensional time-dependent nonlinear PDEs on tensor manifolds of constant rank, with no need for rank reduction methods. We also propose new algorithms for dynamic addition and removal of modes within each subspace. Numerical examples are presented and discussed for high-dimensional hyperbolic and parabolic PDEs in bounded domains. (C) 2019 Elsevier Inc. All rights reserved.
引用
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页数:31
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