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The Local Randomized Neural Networks with Discontinuous Galerkin (LRNN-DG) methods, introduced in [42], were originally designed for solving linear partial differential equations. In this paper, we extend the LRNN-DG methods to solve…

Numerical Analysis · Mathematics 2024-10-01 Jingbo Sun , Fei Wang

In the framework of ODEs, we uncover a new link between the continuous Galerkin method (see Math. Comp. (1972), 26 (118 and 120), 415-426 and 881-891) and the discontinuous Galerkin method (see Mathematical Aspects of Finite elements in…

Numerical Analysis · Mathematics 2025-09-29 Bernardo Cockburn

We propose a deep learning based discontinuous Galerkin method (D2GM) to solve hyperbolic equations with discontinuous solutions and random uncertainties. The main computational challenges for such problems include discontinuities of the…

Numerical Analysis · Mathematics 2021-07-05 Jingrun Chen , Shi Jin , Liyao Lyu

In this paper we present and analyse a discontinuous Galerkin finite element method (DGFEM) for the approximation of solutions to elliptic partial differential equations in nondivergence form, with oblique boundary conditions, on curved…

Numerical Analysis · Mathematics 2018-10-01 Ellya Kawecki

We present a discontinuous Galerkin internal-penalty scheme that is applicable to a large class of linear and nonlinear elliptic partial differential equations. The unified scheme can accommodate all second-order elliptic equations that can…

Numerical Analysis · Mathematics 2022-01-13 Nils L. Fischer , Harald P. Pfeiffer

We introduce a new level-set shape optimization approach based on polytopic (i.e., polygonal in two and polyhedral in three spatial dimensions) discontinuous Galerkin methods. The approach benefits from the geometric mesh flexibility of…

Numerical Analysis · Mathematics 2025-09-05 Raphael S. Fernandes , Emmanuil H. Georgoulis , Alberto Paganini

We begin an investigation of hybridizable discontinuous Galerkin (HDG) methods for approximating the solution of Dirichlet boundary control problems governed by elliptic PDEs. These problems can involve atypical variational formulations,…

Numerical Analysis · Mathematics 2017-12-11 Weiwei Hu , Jiguang Shen , John R. Singler , Yangwen Zhang , Xiaobo Zheng

This paper is concerned with superconvergence properties of the direct discontinuous Galerkin (DDG) method for two-dimensional nonlinear convection-diffusion equations. By using the idea of correction function, we prove that, for any…

Numerical Analysis · Mathematics 2021-11-09 Xinyue Zhang , Waixiang Cao

In this paper we formulate and test numerically a fully-coupled discontinuous Galerkin (DG) method for incompressible two-phase flow with discontinuous capillary pressure. The spatial discretization uses the symmetric interior penalty DG…

Fluid Dynamics · Physics 2013-10-01 Peter Bastian

In this paper we consider discontinuous Galerkin (DG) methods for the incompressible Navier-Stokes equations in the framework of projection methods. In particular we employ symmetric interior penalty DG methods within the second-order…

Numerical Analysis · Mathematics 2017-11-27 Marian Piatkowski , Steffen Müthing , Peter Bastian

Hyperbolic-parabolic partial differential equations are widely used for the modeling of complex, multiscale problems. High-order methods such as the discontinuous Galerkin (DG) scheme are attractive candidates for their numerical…

Numerical Analysis · Mathematics 2025-07-08 Jens Keim , Anna Schwarz , Patrick Kopper , Marcel Blind , Christian Rohde , Andrea Beck

A newly developed weak Galerkin method is proposed to solve parabolic equations. This method allows the usage of totally discontinuous functions in approximation space and preserves the energy conservation law. Both continuous and…

Numerical Analysis · Mathematics 2013-03-18 Qiaoluan H. Li , Junping Wang

In this article, a hybridizable discontinuous Galerkin (HDG) method is proposed and analyzed for the Klein-Gordon equation with local Lipschitz-type non-linearity. {\it A priori} error estimates are derived, and it is proved that…

Numerical Analysis · Mathematics 2024-11-26 Shipra Gupta , Amiya Kumar Pani , Sangita Yadav

Discontinuous Galerkin (DG) methods offer an enormous flexibility regarding local grid refinement and variation of polynomial degrees for a variety of different problem classes. With a focus on diffusion problems, we consider DG…

Numerical Analysis · Mathematics 2013-01-01 Kolja Brix , Claudio Canuto , Wolfgang Dahmen

This work concerns with the discontinuous Galerkin (DG)method for the time-dependent linear elasticity problem. We derive the a posteriori error bounds for semi-discrete and fully discrete problems, by making use of the stationary…

Numerical Analysis · Mathematics 2015-06-11 Thi Hong Cam Luong , Christian Daveau

This paper, as the sequel to previous work, develops numerical schemes for fractional diffusion equations on a two-dimensional finite domain with triangular meshes. We adopt the nodal discontinuous Galerkin methods for the full spatial…

Numerical Analysis · Mathematics 2015-07-14 Liangliang Qiu , Weihua Deng , Jan Hesthaven

In this contribution we present a local discontinuous Galerkin (LDG) pressure-correction scheme for the incompressible Navier-Stokes equations. The scheme does not need penalty parameters and satisfies the discrete continuity equation…

Numerical Analysis · Mathematics 2020-03-30 Janick Thomas Gerstenberger , Samuel Burbulla , Dietmar Kröner

This paper presents high order accurate discontinuous Galerkin (DG) methods for wave problems on moving curved meshes with general choices of basis and quadrature. The proposed method adopts an arbitrary Lagrangian-Eulerian (ALE)…

Numerical Analysis · Mathematics 2020-11-06 Kaihang Guo , Jesse Chan

Presented here is a preliminary study of a strictly linear, discontinuous-Petrov-Galerkin scheme for the discrete-ordinates method in slab geometry. By ``linear'', we mean the discretization does not depend on the solution itself as is the…

Numerical Analysis · Mathematics 2024-03-15 Jeremy A. Roberts

Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research field known as scientific machine learning in which techniques…

Machine Learning · Computer Science 2022-12-12 Junho Choi , Namjung Kim , Youngjoon Hong