Andreas Veeser

Theory of adaptive finite element methods: An introduction

This is a survey on the theory of adaptive finite element methods (AFEM), which are fundamental in modern computational science and engineering. We present a self-contained and up-to-date discussion of AFEM for linear second order elliptic partial differential equations (PDEs) and dimension d>1, with emphasis on the differences and advantages of AFEM over standard FEM. The material is organized in chapters with problems that extend and complement the theory. We start with the functional framework, inf-sup theory, and Petrov-Galerkin method, which are the basis of FEM. We next address four topics of essence in the theory of AFEM that cannot be found in one single article: mesh refinement by bisection, piecewise polynomial approximation in graded meshes, a posteriori error analysis, and convergence and optimal decay rates of AFEM. The first topic is of geometric and combinatorial nature, and describes bisection as a rather simple and efficient technique to create conforming graded meshes with optimal complexity. The second topic explores the potentials of FEM to compensate singular behavior with local resolution and so reach optimal error decay. This theory, although insightful, is insufficient to deal with PDEs since it relies on knowing the exact solution. The third topic provides the missing link, namely a posteriori error estimators, which hinge exclusively on accessible data: we restrict ourselves to the simplest residual-type estimators and present a complete discussion of upper and lower bounds, along with the concept of oscillation and its critical role. The fourth topic refers to the convergence of adaptive loops and its comparison with quasi-uniform refinement. We first show, under rather modest assumptions on the problem class and AFEM, convergence in the natural norm associated to the variational formulation. We next restrict the problem class to coercive symmetric bilinear forms, and show that AFEM is a contraction for a suitable error notion involving the induced energy norm. This property is then instrumental to prove optimal cardinality of AFEM for a class of singular functions, for which the standard FEM is suboptimal.

Efficient and Reliable A Posteriori Error Estimators for Elliptic Obstacle Problems

A posteriori error estimators are derived for linear finite element approximations to elliptic obstacle problems. These estimators yield global upper and local lower bounds for the discretization error. Here discretization error means the sum of two contributions: the distance between continuous and discrete solution in the energy-norm and some quantity that is related to the distance of continuous and discrete contact set. Moreover, the local error indicators in the interior of the discrete contact set reduce to quantities that measure only data resolution.

Pointwise a posteriori error control for elliptic obstacle problems

AbstractWe consider a finite element method for the elliptic obstacle problem over polyhedral domains in ℝd, which enforces the unilateral constraint solely at the nodes. We derive novel optimal upper and lower a posteriori error bounds in the maximum norm irrespective of mesh fineness and the regularity of the obstacle, which is just assumed to be Hölder continuous. They exhibit optimal order and localization to the non-contact set. We illustrate these results with simulations in 2d and 3d showing the impact of localization in mesh grading within the contact set along with quasi-optimal meshes.

Convergent adaptive finite elements for the nonlinear Laplacian

Summary. The numerical solution of the homogeneous Dirichlet problem for the p-Laplacian,

Fully Localized A posteriori Error Estimators and Barrier Sets for Contact Problems

p\in ]1,\infty[

A Unilaterally Constrained Quadratic Minimization with Adaptive Finite Elements

, is considered. We propose an adaptive algorithm with continuous piecewise affine finite elements and prove that the approximate solutions converge to the exact one. While the algorithm is a rather straight-forward generalization of those for the linear case p=2, the proof of its convergence is different. In particular, it does not rely on a strict error reduction.

Primer of Adaptive Finite Element Methods

We derive novel pointwise a posteriori error estimators for elliptic obstacle problems which, except for obstacle resolution, completely vanish within the full-contact set (localization). We then construct a posteriori barrier sets for free boundaries under a natural stability (or nondegeneracy) condition. We illustrate localization properties as well as reliability and efficiency for both solutions and free boundaries via several simulations in 2 and 3 dimensions.

Explicit Upper Bounds for Dual Norms of Residuals

We consider obstacle problems where a quadratic functional associated with the Laplacian is minimized in the set of functions above a possibly discontinuous and thin but piecewise affine obstacle. In order to approximate minimum point and value, we propose an adaptive algorithm that relies on minima with respect to admissible linear finite element functions and on an a posteriori estimator for the error in the minimum value. It is proven that the generated sequence of approximate minima converges to the exact one. Furthermore, our numerical results in two and three dimensions indicate that the convergence rate with respect to the number of degrees of freedom is optimal in that it coincides with the one of nonlinear or adaptive approximation.

Pointwise a posteriori error estimates for monotone semi-linear equations

Adaptive finite element methods (AFEM) are a fundamental numerical instrument in science and engineering to approximate partial differential equations. In the 1980s and 1990s a great deal of effortwas devoted to the design of a posteriori error estimators, following the pioneering work of Babu?ska. These are computable quantities, depending on the discrete solution(s) and data, that can be used to assess the approximation quality and improve it adaptively. Despite their practical success, adaptive processes have been shown to converge, and to exhibit optimal cardinality, only recently for dimension d > 1 and for linear elliptic PDE. These series of lectures presents an up-to-date discussion of AFEM encompassing the derivation of upper and lower a posteriori error bounds for residual-type estimators, including a critical look at the role of oscillation, the design of AFEM and its basic properties, as well as a complete discussion of convergence, contraction property and quasi- optimal cardinality of AFEM

Hierarchical error estimates for the energy functional in obstacle problems

We derive upper bounds for the dual norms of residuals that are explicit in terms of local Poincare constants. Residuals are continuous linear functionals that are orthogonal to a finite element space and have a singular part supported on the skeleton of the underlying mesh. Functionals of this type play a key role in a posteriori error estimation. Our main tools are a discrete partition of unity and suitably weighted trace and Poincare inequalities. The technique is illustrated for negative first order Sobolev norms and a dual norm arising in convection-reaction-diffusion problems.