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Evolve Then Filter Regularization for Stochastic Reduced Order Modeling
Version 1
: Received: 6 August 2018 / Approved: 13 August 2018 / Online: 13 August 2018 (08:12:13 CEST)
A peer-reviewed article of this Preprint also exists.
Xie, X.; Bao, F.; Webster, C.G. Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation. Fluids 2018, 3, 84. Xie, X.; Bao, F.; Webster, C.G. Evolve Filter Stabilization Reduced-Order Model for Stochastic Burgers Equation. Fluids 2018, 3, 84.
Abstract
In this paper, we introduce the evolve-then-filter (EF) regularization method for reduced order modeling of convection-dominated stochastic systems. The standard Galerkin projection reduced order model (G-ROM) yield numerical oscillations in a convection-dominated regime. The evolve-then-filter reduced order model (EF-ROM) aims at the numerical stabilization of the standard G-ROM, which uses explicit ROM spatial filter to regularize various terms in the reduced order model (ROM). Our numerical results based on a stochastic Burgers equation with linear multiplicative noise. It shows that the EF-ROM is significantly better results than G-ROM.
Keywords
reduced order modeling; regularization; fluid dynamics; stochastic Burgers Equation; proper orthogonal decomposition; spatial filter
Subject
Computer Science and Mathematics, Computational Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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