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Related papers: A scalable preconditioner for a DPG method

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Multigrid methods are asymptotically optimal algorithms ideal for large-scale simulations. But, they require making numerous algorithmic choices that significantly influence their efficiency. Unlike recent approaches that learn optimal…

Computational Engineering, Finance, and Science · Computer Science 2024-12-12 Dinesh Parthasarathy , Tommaso Bevilacqua , Martin Lanser , Axel Klawonn , Harald Köstler

This work represents the first endeavor in using ultraweak formulations to implement high-order polygonal finite element methods via the discontinuous Petrov-Galerkin (DPG) methodology. Ultraweak variational formulations are nonstandard in…

Numerical Analysis · Mathematics 2018-06-12 Ali Vaziri Astaneh , Federico Fuentes , Jaime Mora , Leszek Demkowicz

In this article, several discontinuous Petrov-Galerkin (DPG) methods with perfectly matched layers (PMLs) are derived along with their quasi-optimal graph test norms. Ultimately, two different complex coordinate stretching strategies are…

Numerical Analysis · Mathematics 2020-08-11 Ali Vaziri Astaneh , Brendan Keith , Leszek Demkowicz

The discontinuous Galerkin (DG) method has been widely considered in recent years to develop scalable flow solvers for its ability to handle discontinuities, such as shocks and detonations, with greater accuracy and high arithmetic…

Computational Physics · Physics 2025-01-06 Aswin Kumar Arumugam , Konduri Aditya

The Legendre spectral Galerkin method of self-adjoint second order elliptic equations usually results in a linear system with a dense and ill-conditioned coefficient matrix. In this paper, the linear system is solved by a preconditioned…

Numerical Analysis · Mathematics 2020-04-30 Xuhao Diao , Jun Hu , Suna Ma

This paper reviews the adaptive sparse grid discontinuous Galerkin (aSG-DG) method for computing high dimensional partial differential equations (PDEs) and its software implementation. The C\texttt{++} software package called AdaM-DG,…

Numerical Analysis · Mathematics 2022-11-04 Juntao Huang , Wei Guo , Yingda Cheng

We present an optimal preconditioner for the exactly divergence-free discontinuous Galerkin (DG) discretization of Cockburn, Kanschat, and Sch\"otzau [J. Sci. Comput., 31 (2007), pp. 61--73] and Wang and Ye [SIAM J. Numer. Anal., 45 (2007),…

Numerical Analysis · Mathematics 2022-03-29 Sander Rhebergen , Ben S. Southworth

The discontinuous Galerkin (DG) method is widely being used to solve hyperbolic partial differential equations (PDEs) due to its ability to provide high-order accurate solutions in complex geometries, capture discontinuities, and exhibit…

Computational Physics · Physics 2024-07-24 Shubham Kumar Goswami , Konduri Aditya

We propose a least-squares penalization as a means to extend the discontinuous Petrov-Galerkin (DPG) method with optimal test functions to a class of semilinear elliptic problems. The nonlinear contributions are replaced with independent…

Numerical Analysis · Mathematics 2026-04-01 Carlos García Vera , Norbert Heuer , Dirk Praetorius

Discontinuous Galerkin (DG) methods are promising high order discretizations for unsteady compressible flows. Here, we focus on Numerical Weather Prediction (NWP). These flows are characterized by a fine resolution in $z$-direction and low…

Numerical Analysis · Mathematics 2025-06-02 Philipp Birken , Andreas Dedner , Robert Klöfkorn

We analyze the discontinuous Petrov-Galerkin (DPG) method with optimal test functions when applied to solve the Reissner-Mindlin model of plate bending. We prove that the hybrid variational formulation underlying the DPG method is…

Numerical Analysis · Mathematics 2013-01-28 Victor M. Calo , Nathaniel O. Collier , Antti H. Niemi

We investigate the application of the discontinuous Petrov-Galerkin (DPG) finite element framework to stationary convection-diffusion problems. In particular, we demonstrate how the quasi-optimal test space norm can be utilized to improve…

Numerical Analysis · Mathematics 2012-01-10 Antti H. Niemi , Nathaniel O. Collier , Victor M. Calo

In this paper, we propose and analyze an efficient preconditioning method for the elliptic problem based on the reconstructed discontinuous approximation method. We reconstruct a high-order piecewise polynomial space that arbitrary order…

Numerical Analysis · Mathematics 2024-07-16 Ruo Li , Qicheng Liu , Fanyi Yang

Many techniques for real-time trajectory optimization and control require the solution of optimization problems at high frequencies. However, ill-conditioning in the optimization problem can significantly reduce the speed of first-order…

Optimization and Control · Mathematics 2024-09-23 Govind M. Chari , Yue Yu , Behçet Açıkmeşe

A discontinuous Galerkin (dG) method for the numerical solution of initial/boundary value multi-compartment partial differential equation (PDE) models, interconnected with interface conditions, is presented and analysed. The study of…

Numerical Analysis · Mathematics 2013-04-16 Andrea Cangiani , Emmanuil H. Georgoulis , Max Jensen

We investigate various block preconditioners for a low-order Raviart-Thomas discretization of the mixed Poisson problem on adaptive quadrilateral meshes. In addition to standard diagonal and Schur complement preconditioners, we present a…

Numerical Analysis · Mathematics 2024-12-20 Carsten Burstedde , Jose A. Fonseca , Bram Metsch

Gaussian process hyperparameter optimization requires linear solves with, and log-determinants of, large kernel matrices. Iterative numerical techniques are becoming popular to scale to larger datasets, relying on the conjugate gradient…

Machine Learning · Computer Science 2022-06-22 Jonathan Wenger , Geoff Pleiss , Philipp Hennig , John P. Cunningham , Jacob R. Gardner

We present a discontinuous Petrov-Galerkin (DPG) method with optimal test functions for the Reissner-Mindlin plate bending model. Our method is based on a variational formulation that utilizes a Helmholtz decomposition of the shear force.…

Numerical Analysis · Mathematics 2022-05-27 Thomas Führer , Norbert Heuer , Antti H. Niemi

Large linear systems are ubiquitous in modern computational science and engineering. The main recipe for solving them is the use of Krylov subspace iterative methods with well-designed preconditioners. Recently, GNNs have been shown to be a…

Machine Learning · Computer Science 2025-02-04 Vladislav Trifonov , Alexander Rudikov , Oleg Iliev , Yuri M. Laevsky , Ivan Oseledets , Ekaterina Muravleva

In recent years several research efforts focused on the development of high-order discontinuous Galerkin (dG) methods for scale resolving simulations of turbulent flows. Nevertheless, in the context of incompressible flow computations, the…

Computational Physics · Physics 2019-05-14 Matteo Franciolini , Lorenzo Botti , Alessandro Colombo , Andrea Crivellini