Instance optimality of the adaptive maximum strategy
Mathematisches Institut der Universität München（慕尼黑大学数学研究所）；Fakultät für Mathematik, Ruhr-Universität Bochum（波鸿鲁尔大学数学系）；Korteweg-de Vries Institute (KdVI) for Mathematics, University of Amsterdam（阿姆斯特丹大学科特韦格-德弗里斯数学研究所）
In this paper, we prove that the standard adaptive finite element method with a (modified) `maximum marking strategy' is `instance optimal' for the `total error', being the sum of the energy error and the oscillation. This result will be derived in the model setting of Poisson's equation on a polygon, linear finite elements, and conforming triangulations created by newest vertex bisection.
Convergence and optimality of the adaptive Morley element method
LMAM and School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Institute of Computational Mathematics, Chinese Academy of Sciences（中国科学院计算数学研究所）；The School of Mathematical Sciences, Peking University（北京大学数学科学学院）；Department of Mathematics, Pennsylvania State University（宾夕法尼亚州立大学数学系）
In this paper, we present several new a posteriori error estimators and two adaptive mixed finite element methods \textsf{AMFEM1} and \textsf{AMFEM2} for the Hodge Laplacian problem in finite element exterior calculus. We prove that \textsf{AMFEM1} and \textsf{AMFEM2} are both convergent starting from any initial coarse mesh. A suitably defined quasi error is crucial to the convergence analysis. In addition, we prove the optimality of \textsf{AMFEM2}. The main technical contribution is a localized discrete upper bound. As opposed to existing literature, our results work on Lipschitz domains wi…
Convergence and Optimality of Adaptive Mixed Finite Element Methods
University of California at Irvine（加州大学尔湾分校）；University of California at San Diego（加州大学圣地亚哥分校）；Peking University（北京大学）；Pennsylvania State University（宾夕法尼亚州立大学）
Convergence and optimality of the adaptive nonconforming linear element method for the Stokes problem
LMAM and School of Mathematical Sciences, Peking University（北京大学数学科学学院）；The School of Mathematical Sciences and the Beijing International Center for Mathematical Research, Peking University（北京大学数学科学学院和北京国际数学研究中心）；Department of Mathematics, Pennsylvania State University（宾夕法尼亚州立大学数学系）
We consider adaptive finite element methods for second-order elliptic PDEs, where the arising discrete systems are not solved exactly. For contractive iterative solvers, we formulate an adaptive algorithm which monitors and steers the adaptive mesh-refinement as well as the inexact solution of the arising discrete systems. We prove that the proposed strategy leads to linear convergence with optimal algebraic rates. Unlike prior works, however, we focus on convergence rates with respect to the overall computational costs. In explicit terms, the proposed adaptive strategy thus guarantees quasi-o…
Adaptive finite element method for elliptic optimal control problems: convergence and optimality
Institute of Computational Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院计算数学研究所）；Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院系统科学研究所）
We design an adaptive virtual element method (AVEM) of lowest order over triangular meshes with hanging nodes in 2d, which are treated as polygons. AVEM hinges on the stabilization-free a posteriori error estimators recently derived in [8]. The crucial property, that also plays a central role in this paper, is that the stabilization term can be made arbitrarily small relative to the a posteriori error estimators upon increasing the stabilization parameter. Our AVEM concatenates two modules, GALERKIN and DATA. The former deals with piecewise constant data and is shown in [8] to be a contraction…
The subject of this work is a new stochastic Galerkin method for second-order elliptic partial differential equations with random diffusion coefficients. It combines operator compression in the stochastic variables with tree-based spline wavelet approximation in the spatial variables. Relying on a multilevel expansion of the given random diffusion coefficient, the method is shown to achieve optimal computational complexity up to a logarithmic factor. In contrast to existing results, this holds in particular when the achievable convergence rate is limited by the regularity of the random field, …
On $p$-robust convergence and optimality of adaptive FEM driven by equilibrated-flux estimators
Inria（法国国家数字与工程科学研究所）；Univ. Lille（里尔大学）；Laboratoire Paul Painlevé（保罗·庞加莱实验室）；Inria Paris（法国国家数字与工程科学研究所巴黎分部）；Institute for Numerical Simulation, University of Bonn（波恩大学数值模拟研究所）；University of Twente（特文特大学）；Charles University, Faculty of Mathematics and Physics（查理大学数学物理学院）
We prove new optimality results for adaptive mesh refinement algorithms for non-symmetric, indefinite, and time-dependent problems by proposing a generalization of quasi-orthogonality which follows directly from the inf-sup stability of the underlying problem. This completely removes a central technical difficulty in modern proofs of optimal convergence of adaptive mesh refinement algorithms and leads to simple optimality proofs for the Taylor-Hood discretization of the stationary Stokes problem, a finite-element/boundary-element discretization of an unbounded transmission problem, and an adap…
This paper reviews the state of the art and discusses recent developments in the field of adaptive isogeometric analysis, with special focus on the mathematical theory. This includes an overview of available spline technologies for the local resolution of possible singularities as well as the state-of-the-art formulation of convergence and quasi-optimality of adaptive algorithms for both the finite element method (FEM) and the boundary element method (BEM) in the frame of isogeometric analysis (IGA).
Instance-Wise Adaptive Sampling for Dataset Construction in Approximating Inverse Problem Solutions
Center for Computational Mathematics, Flatiron Institute（计算数学中心，Flatiron研究所）；Department of Applied Physics and Applied Mathematics, Columbia University（应用物理与应用数学系，哥伦比亚大学）
