Convergence and Optimality of Adaptive Mixed Methods for Poisson's Equation in the FEEC Framework

Convergence and Optimality of Adaptive Mixed Methods for Poisson's Equation in the FEEC Framework

Year:    2020

Author:    Michael Holst, Yuwen Li, Adam Mihalik, Ryan Szypowski

Journal of Computational Mathematics, Vol. 38 (2020), Iss. 5 : pp. 748–767

Abstract

Finite Element Exterior Calculus (FEEC) was developed by Arnold, Falk, Winther and others over the last decade to exploit the observation that mixed variational problems can be posed on a Hilbert complex, and Galerkin-type mixed methods can then be obtained by solving finite-dimensional subcomplex problems. Chen, Holst, and Xu (Math. Comp. 78 (2009) 35-53) established convergence and optimality of an adaptive mixed finite element method using Raviart-Thomas or Brezzi-Douglas-Marini elements for Poisson's equation on contractible domains in $\mathbb{R}^2$, which can be viewed as a boundary problem on the de Rham complex. Recently Demlow and Hirani (Found. Math. Comput. 14 (2014) 1337-1371) developed fundamental tools for a posteriori analysis on the de Rham complex. In this paper, we use tools in FEEC to construct convergence and complexity results on domains with general topology and spatial dimension. In particular, we construct a reliable and efficient error estimator and a sharper quasi-orthogonality result using a novel technique. Without marking for data oscillation, our adaptive method is a contraction with respect to a total error incorporating the error estimator and data oscillation.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/jcm.1905-m2018-0265

Journal of Computational Mathematics, Vol. 38 (2020), Iss. 5 : pp. 748–767

Published online:    2020-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    20

Keywords:    Finite element exterior calculus Adaptive finite element methods A posteriori error estimates Convergence Quasi-optimality.

Author Details

Michael Holst

Yuwen Li

Adam Mihalik

Ryan Szypowski