100 research outputs found
Convergence and Optimality of Adaptive Mixed Finite Element Methods
The convergence and optimality of adaptive mixed finite element methods for
the Poisson equation are established in this paper. The main difficulty for
mixed finite element methods is the lack of minimization principle and thus the
failure of orthogonality. A quasi-orthogonality property is proved using the
fact that the error is orthogonal to the divergence free subspace, while the
part of the error that is not divergence free can be bounded by the data
oscillation using a discrete stability result. This discrete stability result
is also used to get a localized discrete upper bound which is crucial for the
proof of the optimality of the adaptive approximation
Residual-Based A Posteriori Error Estimates for Symmetric Conforming Mixed Finite Elements for Linear Elasticity Problems
A posteriori error estimators for the symmetric mixed finite element methods
for linear elasticity problems of Dirichlet and mixed boundary conditions are
proposed. Stability and efficiency of the estimators are proved. Finally, we
provide numerical examples to verify the theoretical results
Convergence and optimality of an adaptive modified weak Galerkin finite element method
An adaptive modified weak Galerkin method (AmWG) for an elliptic problem is
studied in this paper, in addition to its convergence and optimality. The weak
Galerkin bilinear form is simplified without the need of the skeletal variable,
and the approximation space is chosen as the discontinuous polynomial space as
in the discontinuous Galerkin method. Upon a reliable residual-based a
posteriori error estimator, an adaptive algorithm is proposed together with its
convergence and quasi-optimality proved for the lowest order case. The major
tool is to bridge the connection between weak Galerkin method and the
Crouzeix-Raviart nonconforming finite element. Unlike the traditional
convergence analysis for methods with a discontinuous polynomial approximation
space, the convergence of AmWG is penalty parameter free
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