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In this paper we present a simple method of deriving a posteriori error equalities and estimates for linear elliptic and parabolic partial differential equations. The error is measured in a combined norm taking into account both the primal…

Numerical Analysis · Mathematics 2017-11-16 Immanuel Anjam , Dirk Pauly

In the present work, a multi-scale framework for neural network enhanced methods is proposed for approximation of function and solution of partial differential equations (PDEs). By introducing the multi-scale concept, the total solution of…

Numerical Analysis · Mathematics 2022-09-07 Xiaodan Ren

In this article we develop convergence theory for a class of goal-oriented adaptive finite element algorithms for second order nonsymmetric linear elliptic equations. In particular, we establish contraction results for a method of this type…

Numerical Analysis · Mathematics 2013-08-09 Michael Holst , Sara Pollock

We consider mixed finite element approximation of a singularly perturbed fourth-order elliptic problem with two different boundary conditions, and present a new measure of the error, whose components are balanced with respect to the…

Numerical Analysis · Mathematics 2016-09-16 Shaohong Du , Runchang Lin , Zhimin Zhang

We discuss goal-oriented adaptivity in the frame of conforming finite element methods and plain convergence of the related a posteriori error estimator for different general marking strategies. We present an abstract analysis for two…

Numerical Analysis · Mathematics 2024-07-30 Valentin Helml , Michael Innerberger , Dirk Praetorius

This work proposes an $r$-adaptive finite element method (FEM) using neural networks (NNs). The method employs the Ritz energy functional as the loss function, currently limiting its applicability to symmetric and coercive problems, such as…

In this paper we propose and analyze a new Multiscale Method for solving semi-linear elliptic problems with heterogeneous and highly variable coefficient functions. For this purpose we construct a generalized finite element basis that spans…

Numerical Analysis · Mathematics 2019-02-20 Patrick Henning , Axel Malqvist , Daniel Peterseim

While many methods exist to discretize nonlinear time-dependent partial differential equations (PDEs), the rigorous estimation and adaptive control of their discretization errors remains challenging. In this paper, we present a methodology…

Numerical Analysis · Mathematics 2017-06-15 Xunxun Wu , Kristoffer van der Zee , Gorkem Simsek , Harald Van Brummelen

In this article we study adaptive finite element methods (AFEM) with inexact solvers for a class of semilinear elliptic interface problems. We are particularly interested in nonlinear problems with discontinuous diffusion coefficients, such…

Numerical Analysis · Mathematics 2016-08-24 Michael Holst , Ryan Szypowski , Yunrong Zhu

A new anisotropic mesh adaptation strategy for finite element solution of elliptic differential equations is presented. It generates anisotropic adaptive meshes as quasi-uniform ones in some metric space, with the metric tensor being…

Numerical Analysis · Mathematics 2019-12-17 Weizhang Huang , Lennard Kamenski , Jens Lang

We present a trust-region-based adaptive finite-element algorithm for numerically solving a class of nonsmooth PDE-constrained optimization problems that includes problems with sparsifying regularizers and convex constraints. In particular,…

Optimization and Control · Mathematics 2026-04-28 Harbir Antil , Robert J. Baraldi , Rohit Khandelwal , Drew P. Kouri

We present an adaptive finite element method for the incompressible Navier--Stokes equations based on a standard splitting scheme (the incremental pressure correction scheme). The presented method combines the efficiency and simplicity of a…

Numerical Analysis · Mathematics 2012-05-15 Kristoffer Selim , Anders Logg , Mats G. Larson

This paper aims to devise an adaptive neural network basis method for numerically solving a second-order semilinear partial differential equation (PDE) with low-regular solutions in two/three dimensions. The method is obtained by combining…

Numerical Analysis · Mathematics 2024-11-05 Jianguo Huang , Haohao Wu , Tao Zhou

We derive a posteriori error estimates for a fully discrete finite element approximation of the stochastic Cahn-Hilliard equation. The a posteriori bound is obtained by a splitting of the equation into a linear stochastic partial…

Numerical Analysis · Mathematics 2022-01-24 Ľubomír Baňas , Christian Vieth

Presence of a high-dimensional stochastic parameter space with discontinuities poses major computational challenges in analyzing and quantifying the effects of the uncertainties in a physical system. In this paper, we propose a stochastic…

Numerical Analysis · Mathematics 2018-08-01 Anindya Bhaduri , Yanyan He , Michael D. Shields , Lori Graham-Brady , Robert M. Kirby

A posteriori error estimator is derived for an elliptic interface problem in the fictitious domain formulation with distributed Lagrange multiplier considering a discontinuous Lagrange multiplier finite element space. A posteriori error…

Numerical Analysis · Mathematics 2024-07-02 Najwa Alshehri , Daniele Boffi , Lucia Gastaldi

We introduce a new Partition of Unity Method for the numerical homogenization of elliptic partial differential equations with arbitrarily rough coefficients. We do not restrict to a particular ansatz space or the existence of a finite…

Numerical Analysis · Mathematics 2016-05-04 Daniel Peterseim , Patrick Henning , Philipp Morgenstern

This article investigates residual a posteriori error estimates and adaptive mesh refinements for time-dependent boundary element methods for the wave equation. We obtain reliable estimates for Dirichlet and acoustic boundary conditions…

Numerical Analysis · Mathematics 2020-10-01 Heiko Gimperlein , Ceyhun Oezdemir , David Stark , Ernst P. Stephan

We establish rigorous \emph{a posteriori} error bounds for a space-time finite element method of arbitrary order discretising linear wave problems in second order formulation. The method combines standard finite elements in space and…

Numerical Analysis · Mathematics 2026-04-24 Zhaonan Dong , Emmanuil H. Georgoulis , Lorenzo Mascotto , Zuodong Wang

Based on neural network and adaptive subspace approximation method, we propose a new machine learning method for solving partial differential equations. The neural network is adopted to build the basis of the finite dimensional subspace.…

Numerical Analysis · Mathematics 2024-12-04 Zhongshuo Lin , Yifan Wang , Hehu Xie