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The development and application of the Discontinuous Galerkin (DG) method have attracted great attention in computational fluid dynamics (CFD) com- munity in the past decades. The underlying reason for such an intensive investigation is due…

Numerical Analysis · Mathematics 2016-01-20 Kun Xu , Chang Liu , Xiaodong Ren

We present and analyze a discontinuous Galerkin method for the numerical modeling of the non-linear fully-coupled thermo-hydro-mechanic problem. We propose a high-order symmetric weighted interior penalty scheme that supports general…

Numerical Analysis · Mathematics 2023-11-28 Stefano Bonetti , Michele Botti , Paola F. Antonietti

We develop a multi-dimensional hybrid discontinuous Galerkin method for multi-scale kinetic equations. This method is based on moment realizability matrices, a concept introduced by D. Levermore, W. Morokoff and B. Nadiga for one…

Numerical Analysis · Mathematics 2018-08-01 Francis Filbet , Tao Xiong

Finite element schemes based on discontinuous Galerkin methods possess features amenable to massively parallel computing accelerated with general purpose graphics processing units (GPUs). However, the computational performance of such…

Computational Physics · Physics 2016-04-20 Axel Modave , Amik St-Cyr , Tim Warburton

This work discusses the application of an affine reconstructed nodal DG method for unstructured grids of triangles. Solving the diffusion terms in the DG method is non-trivial due to the solution representations being piecewise continuous.…

Computational Physics · Physics 2021-04-20 Yang Song , Bhuvana Srinivasan

High order accurate and explicit time-stable solvers are well suited for hyperbolic wave propagation problems. As a result of the complexities of real geometries, internal interfaces and nonlinear boundary and interface conditions,…

Numerical Analysis · Mathematics 2021-04-13 Kenneth Duru , Leonhard Rannabauer , Alice-Agnes Gabriel , On Ki Angel Ling , Heiner Igel , Michael Bader

We propose a new method, the continuous Galerkin method with globally and locally supported basis functions (CG-GL), to address the parametric robustness issues of reduced-order models (ROMs) by incorporating solution-based adaptivity with…

Numerical Analysis · Mathematics 2023-10-10 Han Gao , Matthew J. Zahr

Discontinuous Galerkin (DG) methods for the numerical solution of partial differential equations have enjoyed considerable success because they are both flexible and robust: They allow arbitrary unstructured geometries and easy control of…

Numerical Analysis · Mathematics 2009-11-18 Andreas Klöckner , Tim Warburton , Jeffrey Bridge , Jan S. Hesthaven

A framework for performing dynamic mesh adaptation with the discontinuous Galerkin method (DGM) is presented. Adaptations include modifications of the local mesh step size (h-adaptation) and the local degree of the approximating polynomials…

Computational Physics · Physics 2013-01-29 Sascha M. Schnepp , Thomas Weiland

We develop a novel cut discontinuous Galerkin (CutDG) method for stationary advection-reaction problems on surfaces embedded in $\mathbb{R}^d$. The CutDG method is based on embedding the surface into a full-dimensional background mesh and…

Numerical Analysis · Mathematics 2023-04-13 Tale Bakken Ulfsby , André Massing , Simon Sticko

We propose a family of high-order local discontinuous Galerkin (LDG) methods, built on a parametric representation and coupled with a semi-implicit backward Euler time discretization, for isotropic and anisotropic curve-shortening flows.…

Numerical Analysis · Mathematics 2026-04-06 Xiuhui Guo , Wei Jiang , Chunmei Su

We combine continuous and discontinuous Galerkin methods in the setting of a model diffusion problem. Starting from a hybrid discontinuous formulation, we replace element interiors by more general subsets of the computational domain -…

Computational Physics · Physics 2018-11-30 Martin Vymazal , David Moxey , Chris Cantwell , Spencer Sherwin , Robert M. Kirby

Achieving a substantial part of peak performance on todays and future high-performance computing systems is a major challenge for simulation codes. In this paper we address this question in the context of the numerical solution of partial…

Numerical Analysis · Mathematics 2017-11-30 Steffen Müthing , Marian Piatkowski , Peter Bastian

Dynamical low-rank approximation (DLRA) is an emerging tool for reducing computational costs and provides memory savings when solving high-dimensional problems. In this work, we propose and analyze a semi-implicit dynamical low-rank…

Numerical Analysis · Mathematics 2023-08-14 Peimeng Yin , Eirik Endeve , Cory D. Hauck , Stefan R. Schnake

In this paper, we propose a general meshless structure-preserving Galerkin method for solving dissipative PDEs on surfaces. By posing the PDE in the variational formulation and simulating the solution in the finite-dimensional approximation…

Numerical Analysis · Mathematics 2024-12-19 Zhengjie Sun , Leevan Ling , Meng Chen

In this paper, we consider a class of discontinuous Galerkin (DG) methods for one-dimensional nonlocal diffusion (ND) problems. The nonlocal models, which are integral equations, are widely used in describing many physical phenomena with…

Numerical Analysis · Mathematics 2024-08-15 Qiang Du , Lili Ju , Jianfang Lu , Xiaochuan Tian

In (Dzanic, J. Comp. Phys., 508:113010, 2024), a limiting approach for high-order discontinuous Galerkin schemes was introduced which allowed for imposing constraints on the solution continuously (i.e., everywhere within the element). While…

Numerical Analysis · Mathematics 2024-08-21 Tarik Dzanic

We propose and analyze a seamless extended Discontinuous Galerkin (DG) discretization of advection-diffusion equations on semi-infinite domains. The semi-infinite half line is split into a finite subdomain where the model uses a standard…

Numerical Analysis · Mathematics 2021-07-23 Federico Vismara , Tommaso Benacchio , Luca Bonaventura

In recent years, deep learning methods, exemplified by Physics-Informed Neural Networks (PINNs), have been widely applied to the numerical solution of differential equations. However, these methods may suffer from limited accuracy, high…

Numerical Analysis · Mathematics 2026-03-17 Tao Tang , Jiang Yang , Yuxiang Zhao , Quanhui Zhu

Deep neural networks have been shown to provide accurate function approximations in high dimensions. However, fitting network parameters requires informative training data that are often challenging to collect in science and engineering…

Numerical Analysis · Mathematics 2024-03-01 Joan Bruna , Benjamin Peherstorfer , Eric Vanden-Eijnden