98 research outputs found
Direct guaranteed lower eigenvalue bounds with quasi-optimal adaptive mesh-refinement
Garantierte untere Eigenwertschranken (GLB) für elliptische Eigenwertprobleme partieller Differentialgleichungen sind in der Theorie sowie in praktischen Anwendungen relevant. Auf Grund des Rayleigh-Ritz- (oder) min-max-Prinzips berechnen alle konformen Finite-Elemente-Methoden (FEM) garantierte obere Schranken. Ein Postprocessing nichtkonformer Methoden von Carstensen und Gedicke (Math. Comp., 83.290, 2014) sowie Carstensen und Gallistl (Numer. Math., 126.1, 2014) berechnet GLB. In diesen Schranken ist die maximale Netzweite ein globaler Parameter, das kann bei adaptiver Netzverfeinerung zu deutlichen Unterschätzungen führen. In einigen numerischen Beispielen versagt dieses Postprocessing für lokal verfeinerte Netze komplett. Diese Dissertation präsentiert, inspiriert von einer neuen skeletal-Methode von Carstensen, Zhai und Zhang (SIAM J. Numer. Anal., 58.1, 2020), einerseits eine modifizierte hybrid-high-order Methode (m=1) und andererseits ein allgemeines Framework für extra-stabilisierte nichtkonforme Crouzeix-Raviart (m=1) bzw. Morley (m=2) FEM. Diese neuen Methoden berechnen direkte GLB für den m-Laplace-Operator, bei denen eine leicht überprüfbare Bedingung an die maximale Netzweite garantiert, dass der k-te diskrete Eigenwert eine untere Schranke für den k-ten Dirichlet-Eigenwert ist. Diese GLB-Eigenschaft und a priori Konvergenzraten werden für jede Raumdimension etabliert. Der neu entwickelte Ansatz erlaubt adaptive Netzverfeinerung, die für optimale Konvergenzraten auch bei nichtglatten Eigenfunktionen erforderlich ist. Die Überlegenheit der neuen adaptiven FEM wird durch eine Vielzahl repräsentativer numerischer Beispiele illustriert. Für die extra-stabilisierte GLB wird bewiesen, dass sie mit optimalen Raten gegen einen einfachen Eigenwert konvergiert, indem die Axiome der Adaptivität von Carstensen, Feischl, Page und Praetorius (Comput. Math. Appl., 67.6, 2014) sowie Carstensen und Rabus (SIAM J. Numer. Anal., 55.6, 2017) verallgemeinert werden.Guaranteed lower eigenvalue bounds (GLB) for elliptic eigenvalue problems of partial differential equation are of high relevance in theory and praxis. Due to the Rayleigh-Ritz (or) min-max principle all conforming finite element methods (FEM) provide guaranteed upper eigenvalue bounds. A post-processing for nonconforming FEM of Carstensen and Gedicke (Math. Comp., 83.290, 2014) as well as Carstensen and Gallistl (Numer. Math., 126.1,2014) computes GLB. However, the maximal mesh-size enters as a global parameter in the eigenvalue bound and may cause significant underestimation for adaptive mesh-refinement. There are numerical examples, where this post-processing on locally refined meshes fails completely. Inspired by a recent skeletal method from Carstensen, Zhai, and Zhang (SIAM J. Numer. Anal., 58.1, 2020) this thesis presents on the one hand a modified hybrid high-order method (m=1) and on the other hand a general framework for an extra-stabilized nonconforming Crouzeix-Raviart (m=1) or Morley (m=2) FEM. These novel methods compute direct GLB for the m-Laplace operator in that a specific smallness assumption on the maximal mesh-size guarantees that the computed k-th discrete eigenvalue is a lower bound for the k-th Dirichlet eigenvalue. This GLB property as well as a priori convergence rates are established in any space dimension. The novel ansatz allows for adaptive mesh-refinement necessary to recover optimal convergence rates for non-smooth eigenfunctions. Striking numerical evidence indicates the superiority of the new adaptive eigensolvers. For the extra-stabilized nonconforming methods (a generalization of) known abstract arguments entitled as the axioms of adaptivity from Carstensen, Feischl, Page, and Praetorius (Comput. Math. Appl., 67.6, 2014) as well as Carstensen and Rabus (SIAM J. Numer. Anal., 55.6, 2017) allow to prove the convergence of the GLB towards a simple eigenvalue with optimal rates
The Lower Bounds for Eigenvalues of Elliptic Operators --By Nonconforming Finite Element Methods
The aim of the paper is to introduce a new systematic method that can produce
lower bounds for eigenvalues. The main idea is to use nonconforming finite
element methods. The general conclusion herein is that if local approximation
properties of nonconforming finite element spaces are better than global
continuity properties of , corresponding methods will produce lower bounds
for eigenvalues. More precisely, under three conditions on continuity and
approximation properties of nonconforming finite element spaces we first show
abstract error estimates of approximate eigenvalues and eigenfunctions.
Subsequently, we propose one more condition and prove that it is sufficient to
guarantee nonconforming finite element methods to produce lower bounds for
eigenvalues of symmetric elliptic operators. As one application, we show that
this condition hold for most nonconforming elements in literature. As another
important application, this condition provides a guidance to modify known
nonconforming elements in literature and to propose new nonconforming elements.
In fact, we enrich locally the Crouzeix-Raviart element such that the new
element satisfies the condition; we propose a new nonconforming element for
second order elliptic operators and prove that it will yield lower bounds for
eigenvalues. Finally, we prove the saturation condition for most nonconforming
elements.Comment: 24 page
Recovery Techniques For Finite Element Methods And Their Applications
Recovery techniques are important post-processing methods to obtain improved approximate solutions from primary data with reasonable cost. The practical us- age of recovery techniques is not only to improve the quality of approximation, but also to provide an asymptotically exact posteriori error estimators for adaptive meth- ods. This dissertation presents recovery techniques for nonconforming finite element methods and high order derivative as well as applications of gradient recovery.
Our first target is to develop a systematic gradient recovery technique for Crouzeix- Raviart element. The proposed method uses finite element solution to build a better approximation of the exact gradient based on local least square fittings. Due to poly- nomial preserving property of least square fitting, it is easy to show that the new proposed method preserves quadratic polynomials. In addition, the proposed gra- dient recovery is linearly bounded. Numerical tests indicate the recovered gradient is superconvergent to the exact gradient for both second order elliptic equation and Stokes equation. The gradient recovery technique can be used in a posteriori error
estimates for Crouzeix-Raviart element, which is relatively simple to implement and problem independent.
Our second target is to propose and analyze a new effective Hessian recovery for continuous finite element of arbitrary order. The proposed Hessian recovery is based on polynomial preserving recovery. The proposed method preserves polynomials of degree (k + 1) on general unstructured meshes and polynomials of degree (k + 2) on translation invariant meshes. Based on it polynomial preserving property, we can able to prove superconvergence of the proposed method on mildly structured meshes. In addition, we establish the ultraconvergence result for the new Hessian recovery technique on translation invariant finite element space of arbitrary order.
Our third target is to demonstrate application of gradient recovery in eigenvalue computation. We propose two superconvergent two-grid methods for elliptic eigen- value problems by taking advantage of two-gird method, two-space method, shifted- inverse power method, and gradient recovery enhancement. Theoretical and numer- ical results reveal that the proposed methods provide superconvergent eigenfunction approximation and ultraconvergent eigenvalue approximation. In addition, two mul- tilevel adaptive methods based recovery type a posterior error estimate are proposed
Nonconforming finite element approximations of the Steklov eigenvalue problem and its lower bound approximations
summary:The paper deals with error estimates and lower bound approximations of the Steklov eigenvalue problems on convex or concave domains by nonconforming finite element methods. We consider four types of nonconforming finite elements: Crouzeix-Raviart, , and enriched Crouzeix-Raviart. We first derive error estimates for the nonconforming finite element approximations of the Steklov eigenvalue problem and then give the analysis of lower bound approximations. Some numerical results are presented to validate our theoretical results
Supercloseness and asymptotic analysis of the Crouzeix-Raviart and enriched Crouzeix-Raviart elements for the Stokes problem
For the Crouzeix-Raviart and enriched Crouzeix-Raviart elements, asymptotic
expansions of eigenvalues of the Stokes operator are derived by establishing
two pseudostress interpolations, which admit a full one-order supercloseness
with respect to the numerical velocity and the pressure, respectively. The
design of these interpolations overcomes the difficulty caused by the lack of
supercloseness of the canonical interpolations for the two nonconforming
elements, and leads to an intrinsic and concise asymptotic analysis of
numerical eigenvalues, which proves an optimal superconvergence of eigenvalues
by the extrapolation algorithm. Meanwhile, an optimal superconvergence of
postprocessed approximations for the Stokes equation is proved by use of this
supercloseness. Finally, numerical experiments are tested to verify the
theoretical results
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