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We present a comparison of different multigrid approaches for the solution of systems arising from high-order continuous finite element discretizations of elliptic partial differential equations on complex geometries. We consider the…

Numerical Analysis · Mathematics 2015-03-09 Hari Sundar , Georg Stadler , George Biros

We present an efficient matrix-free geometric multigrid method for the elastic Helmholtz equation, and a suitable discretization. Many discretization methods had been considered in the literature for the Helmholtz equations, as well as many…

Numerical Analysis · Mathematics 2023-12-05 Rachel Yovel , Eran Treister

Numerical models based on partial differential equations (PDE), or integro-differential equations, are ubiquitous in engineering and science, making it possible to understand or design systems for which physical experiments would be…

Computational Physics · Physics 2021-04-02 Julien Bect , Souleymane Zio , Guillaume Perrin , Claire Cannamela , Emmanuel Vazquez

This paper introduces a novel approach to algebraic multigrid methods for large systems of linear equations coming from finite element discretizations of certain elliptic second order partial differential equations. Based on a discrete…

Numerical Analysis · Mathematics 2020-11-30 Lukas Kogler , Joachim Schöberl

We adopt the integral definition of the fractional Laplace operator and analyze solution techniques for fractional, semilinear, and elliptic optimal control problems posed on Lipschitz polytopes. We consider two strategies of…

Numerical Analysis · Mathematics 2023-03-02 Enrique Otarola

In this paper we describe in detail the computational algorithm used by our parallel multigrid elliptic equation solver with adaptive mesh refinement. Our code uses truncation error estimates to adaptively refine the grid as part of the…

General Relativity and Quantum Cosmology · Physics 2009-11-10 J. David Brown , Lisa L. Lowe

Recently, rectified flow (RF)-based models have achieved state-of-the-art performance in many areas for both the multi-step and one-step generation. However, only a few theoretical works analyze the discretization complexity of RF-based…

Machine Learning · Computer Science 2025-08-13 Ruofeng Yang , Zhaoyu Zhu , Bo Jiang , Cheng Chen , Shuai Li

A local weighted discontinuous Galerkin gradient discretization method for solving elliptic equations is introduced. The local scheme is based on a coarse grid and successively improves the solution solving a sequence of local elliptic…

Numerical Analysis · Mathematics 2018-07-30 Assyr Abdulle , Giacomo Rosilho de Souza

We generalize our earlier results concerning meshfree collocation methods for semilinear elliptic second order problems to the quasilinear case. The stability question, however, is treated differently, namely by extending a paper on…

Numerical Analysis · Mathematics 2018-06-19 Klaus Böhmer , Robert Schaback

An efficient linear solver plays an important role while solving partial differential equations (PDEs) and partial integro-differential equations (PIDEs) type mathematical models. In most cases, the efficiency depends on the stability and…

Numerical Analysis · Mathematics 2013-04-15 Samir Kumar Bhowmik

We propose an adaptive multigrid preconditioning technology for solving linear systems arising from Discontinuous Petrov-Galerkin (DPG) discretizations. Unlike standard multigrid techniques, this preconditioner involves only trace spaces…

Numerical Analysis · Mathematics 2020-10-15 Socratis Petrides , Leszek Demkowicz

We consider second-order PDE problems set in unbounded domains and discretized by Lagrange finite elements on a finite mesh, thus introducing an artificial boundary in the discretization. Specifically, we consider the reaction diffusion…

Numerical Analysis · Mathematics 2025-03-31 T. Chaumont-Frelet

A discretization scheme for variable coefficient elliptic PDEs in the plane is presented. The scheme is based on high-order Gaussian quadratures and is designed for problems with smooth solutions, such as scattering problems involving soft…

Numerical Analysis · Mathematics 2015-03-17 Per-Gunnar Martinsson

In this paper, we propose a $W$-cycle $p$-multigrid method for solving the $p$-version symmetric interior penalty discontinuous Galerkin (SIPDG) discretization of elliptic problems. This SIPDG discretization employs hierarchical Legendre…

Numerical Analysis · Mathematics 2025-09-18 Nuo Lei , Donghang Zhang , Weiying Zheng

In this paper, elliptic control problems with pointwise box constraints on the state is considered, where the corresponding Lagrange multipliers in general only represent regular Borel measure functions. To tackle this difficulty, the…

Optimization and Control · Mathematics 2017-06-19 Zixuan Chen , Xiaoliang Song , Xuping Zhang , Bo Yu

This paper is concerned with the decoupling of delayed linear forward-backward stochastic differential equations (D-FBSDEs), which is much more involved than the delay-free case due to the infinite dimension caused by the delay. A new…

Optimization and Control · Mathematics 2020-09-23 Tianfu Ma , Juanjuan Xu , Huanshui Zhang

We examine what is an efficient and scalable nonlinear solver, with low work and memory complexity, for many classes of discretized partial differential equations (PDEs) - matrix-free Full multigrid (FMG) with a Full Approximation Storage…

Numerical Analysis · Mathematics 2023-06-07 Mark F. Adams

We investigate the performance of algebraic multigrid methods for the solution of the linear system of equations arising from a Virtual Element discretization. We provide numerical experiments on very general polygonal meshes for a model…

Numerical Analysis · Mathematics 2018-12-06 Daniele Prada , Micol Pennacchio

An efficient $hp$-multigrid scheme is presented for local discontinuous Galerkin (LDG) discretizations of elliptic problems, formulated around the idea of separately coarsening the underlying discrete gradient and divergence operators. We…

Numerical Analysis · Mathematics 2019-03-14 Daniel Fortunato , Chris H. Rycroft , Robert Saye

Neural networks have shown significant potential in solving partial differential equations (PDEs). While deep networks are capable of approximating complex functions, direct one-shot training often faces limitations in both accuracy and…

Numerical Analysis · Mathematics 2025-03-10 Mingxing Weng , Zhiping Mao , Jie Shen