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This paper presents a novel Stabilizer-Free weak Galerkin (WG) finite element method for solving the Brinkman equations without the need for conventional stabilization techniques. The Brinkman model, which mathematically blends features of…

Numerical Analysis · Mathematics 2025-07-28 Chunmei Wang , Shangyou Zhang

We analyze the conjugate gradient (CG) method with variable preconditioning for solving a linear system with a real symmetric positive definite (SPD) matrix of coefficients $A$. We assume that the preconditioner is SPD on each step, and…

Numerical Analysis · Mathematics 2007-12-24 Andrew V. Knyazev , Ilya Lashuk

The Stokes equations play an important role in the incompressible flow simulation. In this paper, a novel divergence-free parametric mixed finite element method is proposed for solving three-dimensional Stokes equations on domains with…

Numerical Analysis · Mathematics 2025-12-19 Lingxiao Li , Haiyan Su , He Zhang , Weiying Zheng

We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a robust full-space interior-point (IP)-Gauss-Newton…

Optimization and Control · Mathematics 2024-10-22 Tucker Hartland , Cosmin G. Petra , Noemi Petra , Jingyi Wang

Finite element discretization of Stokes problems can result in singular, inconsistent saddle point linear algebraic systems. This inconsistency can cause many iterative methods to fail to converge. In this work, we consider the lowest-order…

Numerical Analysis · Mathematics 2024-12-16 Weizhang Huang , Zhuoran Wang

In this work, solvers and preconditioners based on Gauss-Seidel and Jacobi algorithms are explored for stochastic Galerkin discretization of partial differential equations (PDEs) with random input data. Gauss-Seidel and Jacobi algorithms…

Numerical Analysis · Mathematics 2019-04-16 Ramakrishna Tipireddy , Eric T. Phipps , Roger G. Ghanem

In this paper we study proximal conditional-gradient (CG) and proximal gradient-projection type algorithms for a block-structured constrained nonconvex optimization model, which arises naturally from tensor data analysis. First, we…

Optimization and Control · Mathematics 2014-10-16 Bo Jiang , Shuzhong Zhang

A discretization method with non-matching grids is proposed for the coupled Stokes-Darcy problem that uses a mortar variable at the interface to couple the marker and cell (MAC) method in the Stokes domain with the Raviart-Thomas mixed…

Numerical Analysis · Mathematics 2024-02-19 Wietse M. Boon , Dennis Gläser , Rainer Helmig , Kilian Weishaupt , Ivan Yotov

We present optimal preconditioners for a recently introduced hybridized discontinuous Galerkin finite element discretization of the Stokes equations. Typical of hybridized discontinuous Galerkin methods, the method has degrees-of-freedom…

Numerical Analysis · Mathematics 2023-07-06 Sander Rhebergen , Garth N. Wells

We derive novel, fast, and parameter-robust preconditioned iterative methods for steady and time-dependent Navier--Stokes control problems. Our approach may be applied to time-dependent problems which are discretized using backward Euler or…

Numerical Analysis · Mathematics 2021-08-03 Santolo Leveque , John W. Pearson

The stochastic inverse eigenvalue problem aims to reconstruct a stochastic matrix from its spectrum. While there exists a large literature on the existence of solutions for special settings, there are only few numerical solution methods…

Numerical Analysis · Mathematics 2020-04-17 Gabriele Steidl , Maximilian Winkler

This article considers the iterative solution of a finite element discretisation of the magma dynamics equations. In simplified form, the magma dynamics equations share some features of the Stokes equations. We therefore formulate, analyse…

Numerical Analysis · Mathematics 2016-07-08 Sander Rhebergen , Garth N. Wells , Richard F. Katz , Andrew J. Wathen

The goal of this paper is to create a fruitful bridge between the numerical methods for approximating partial differential equations (PDEs) in fluid dynamics and the (iterative) numerical methods for dealing with the resulting large linear…

Numerical Analysis · Mathematics 2016-12-15 M. Dumbser , F. Fambri , I. Furci , M. Mazza , M. Tavelli , S. Serra-Capizzano

We present preconditioning techniques to solve linear systems of equations with a block two-by-two and three-by-three structure arising from finite element discretizations of the fictitious domain method with Lagrange multipliers. In…

Numerical Analysis · Mathematics 2026-03-09 Michele Benzi , Marco Feder , Luca Heltai , Federica Mugnaioni

The recent article "A Bayesian conjugate gradient method" by Cockayne, Oates, Ipsen, and Girolami proposes an approximately Bayesian iterative procedure for the solution of a system of linear equations, based on the conjugate gradient…

Numerical Analysis · Mathematics 2019-06-26 T. J. Sullivan

The linear conjugate gradient method is widely used in physical simulation, particularly for solving large-scale linear systems derived from Newton's method. The nonlinear conjugate gradient method generalizes the conjugate gradient method…

Optimization and Control · Mathematics 2024-05-15 Xing Shen , Runyuan Cai , Mengxiao Bi , Tangjie Lv

Stokes flow equations have been implemented successfully in practice for simulating problems with moving interfaces. Though computational methods produce accurate solutions and numerical convergence can be demonstrated using a resolution…

Numerical Analysis · Mathematics 2023-02-17 Haixia Dong , Zhongshu Zhao , Shuwang Li , Wenjun Ying , Jiwei Zhang

In this paper we propose a novel staggered discontinuous Galerkin method for the Brinkman problem on general quadrilateral and polygonal meshes. The proposed method is robust in the Stokes and Darcy limits, in addition, hanging nodes can be…

Numerical Analysis · Mathematics 2020-04-22 Lina Zhao , Eric Chung , Ming Fai Lam

A discrete divergence-free weak Galerkin finite element method is developed for the Stokes equations based on a weak Galerkin (WG) method introduced in the reference [15]. Discrete divergence-free bases are constructed explicitly for the…

Numerical Analysis · Mathematics 2016-03-01 Lin Mu , Junping Wang , Xiu Ye

We present a novel deep learning approach to approximate the solution of large, sparse, symmetric, positive-definite linear systems of equations. These systems arise from many problems in applied science, e.g., in numerical methods for…

Machine Learning · Computer Science 2022-10-04 Ayano Kaneda , Osman Akar , Jingyu Chen , Victoria Kala , David Hyde , Joseph Teran