Incremental proper orthogonal decomposition for PDE simulation data

被引:21
|
作者
Fareed, Hiba [1 ]
Singler, John R. [1 ]
Zhang, Yangwen [1 ]
Shen, Jiguang [2 ]
机构
[1] Missouri Univ Sci & Technol, Dept Math & Stat, Rolla, MO 65409 USA
[2] Univ Minnesota, Sch Math, Minneapolis, MN 55455 USA
基金
美国国家科学基金会;
关键词
Proper orthogonal decomposition; Incremental algorithm; Weighted norm; Finite element method; MODELS;
D O I
10.1016/j.camwa.2017.09.012
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
We propose an incremental algorithm to compute the proper orthogonal decomposition (POD) of simulation data for a partial differential equation. Specifically, we modify an incremental matrix SVD algorithm of Brand to accommodate data arising from Galerkin-type simulation methods for time dependent PDEs. The algorithm is applicable to data generated by many numerical methods for PDEs, including finite element and discontinuous Galerkin methods. The algorithm initializes and efficiently updates the dominant POD eigenvalues and modes during the time stepping in a PDE solver without storing the simulation data. We prove that the algorithm without truncation updates the POD exactly. We demonstrate the effectiveness of the algorithm using finite element computations for a 1D Burgers' equation and a 2D Navier-Stokes problem. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1942 / 1960
页数:19
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