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The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural networks (VPINNs). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy is based on…

Numerical Analysis · Mathematics 2024-01-11 Jamie M. Taylor , Manuela Bastidas , Victor M. Calo , David Pardo

In this work, we develop algebraic solvers for linear systems arising from the discretization of second-order elliptic partial differential equations by saddle-point mixed finite element methods of arbitrary polynomial degree $p \ge 0$ on…

Numerical Analysis · Mathematics 2026-02-03 Ani Miraçi , Jan Papež , Martin Vohralík , Ivan Yotov

We prove the convergence of greedy and randomized versions of Schwarz iterative methods for solving linear elliptic variational problems based on infinite space splittings of a Hilbert space. For the greedy case, we show a squared error…

Numerical Analysis · Mathematics 2015-11-02 Michael Griebel , Peter Oswald

In this work, we derive two-sided a posteriori error estimates for the dual-weighted residual (DWR) method. We consider both single and multiple goal functionals. Using a saturation assumption, we derive lower bounds yielding the efficiency…

Numerical Analysis · Mathematics 2018-11-20 Bernhard Endtmayer , Ulrich Langer , Thomas Wick

In this work, a local Fourier analysis is presented to study the convergence of multigrid methods based on additive Schwarz smoothers. This analysis is presented as a general framework which allows us to study these smoothers for any type…

We derive optimal order a posteriori error estimates for fully discrete approximations of linear Schr\"odinger-type equations, in the $L^\infty(L^2)-$norm. For the discretization in time we use the Crank-Nicolson method, while for the space…

Numerical Analysis · Mathematics 2013-04-10 Theodoros Katsaounis , Irene Kyza

In this paper, based on the overlapping domain decomposition method (DDM) proposed in \cite{Leng2015}, an one step preconditioner is proposed to solve 2D high frequency Helmholtz equation. The computation domain is decomposed in both $x$…

Numerical Analysis · Mathematics 2015-08-13 Wei Leng , Lili Ju

We propose an a posteriori error estimator for high-order $p$- or $hp$-finite element discretizations of selfadjoint linear elliptic eigenvalue problems that is appropriate for estimating the error in the approximation of an eigenvalue…

Numerical Analysis · Mathematics 2020-09-16 Stefano Giani , Luka Grubisic , Harri Hakula , Jeffrey Ovall

Domain decomposition methods are essential in solving applied problems on parallel computer systems. For boundary value problems for evolutionary equations the implicit schemes are in common use to solve problems at a new time level…

Numerical Analysis · Computer Science 2011-01-13 Petr N. Vabishchevich

In recent years, SPDEs have become a well-studied field in mathematics. With their increase in popularity, it becomes important to efficiently approximate their solutions. Thus, our goal is a contribution towards the development of…

Numerical Analysis · Mathematics 2024-01-17 Evelyn Buckwar , Ana Djurdjevac , Monika Eisenmann

Contact phenomena are essential in understanding the behavior of mechanical systems. Existing computational approaches for simulating mechanical contact often encounter numerical issues, such as inaccurate physical predictions, energy…

Computational Engineering, Finance, and Science · Computer Science 2023-11-13 A. Mota , D. Koliesnikova , I. Tezaur , J. Hoy

Domain decomposition methods are among the most efficient for solving sparse linear systems of equations. Their effectiveness relies on a judiciously chosen coarse space. Originally introduced and theoretically proved to be efficient for…

Numerical Analysis · Mathematics 2022-01-10 Hussam Al Daas , Pierre Jolivet , Tyrone Rees

We examine the use of the Dirichlet-to-Neumann coarse space within an additive Schwarz method to solve the Helmholtz equation in 2D. In particular, we focus on the selection of how many eigenfunctions should go into the coarse space. We…

Numerical Analysis · Mathematics 2021-07-08 Niall Bootland , Victorita Dolean

We investigate additive Schwarz methods for semilinear elliptic problems with convex energy functionals, which have wide scientific applications. A key observation is that the convergence rates of both one- and two-level additive Schwarz…

Numerical Analysis · Mathematics 2024-07-09 Jongho Park

The modern design of industrial structures leads to very complex simulations characterized by nonlinearities, high heterogeneities, tortuous geometries... Whatever the modelization may be, such an analysis leads to the solution to a family…

Numerical Analysis · Mathematics 2012-08-22 Pierre Gosselet , Christian Rey

This paper derives optimal coefficients for optimized Schwarz iterations for the time-dependent Stokes-Darcy problem using an innovative strategy to solve a nonstandard min-max problem. The coefficients take into account both physical and…

Numerical Analysis · Mathematics 2023-05-15 Marco Discacciati , Tommaso Vanzan

Adaptive atomistic/continuum (a/c) coupling method is an important method for the simulation of material and atomistic systems with defects to achieve the balance of accuracy and efficiency. Residual based a posteriori error estimator is…

Numerical Analysis · Mathematics 2022-11-28 Yangshuai Wang , Hao Wang

A local approach to the time integration of PDEs by exponential methods is proposed, motivated by theoretical estimates by A.Iserles on the decay of off-diagonal terms in the exponentials of sparse matrices. An overlapping domain…

Numerical Analysis · Mathematics 2015-05-12 Luca Bonaventura

We study for the first time Schwarz domain decomposition methods for the solution of the Navier equations modeling the propagation of elastic waves. These equations in the time harmonic regime are difficult to solve by iterative methods,…

Numerical Analysis · Mathematics 2019-04-30 Romain Brunet , Victorita Dolean , Martin J. Gander

Neural networks (NNs) have gained significant attention across various engineering disciplines, particularly in design optimization, where they are used to build surrogate models for high-dimensional regression problems. Despite their power…

Computational Engineering, Finance, and Science · Computer Science 2026-03-30 Timm Gödde , Eisso H. Atzema , Bojana Rosić