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We present a general family of subcell limiting strategies to construct robust high-order accurate nodal discontinuous Galerkin (DG) schemes. The main strategy is to construct compatible low order finite volume (FV) type discretizations…

Numerical Analysis · Mathematics 2022-11-11 Andrés M. Rueda-Ramírez , Will Pazner , Gregor J. Gassner

Fast Fourier transform (FFT) based methods have turned out to be an effective computational approach for numerical homogenisation. In particular, Fourier-Galerkin methods are computational methods for partial differential equations that are…

Numerical Analysis · Mathematics 2020-04-22 Jaroslav Vondřejc , Dishi Liu , Martin Ladecký , Hermann G. Matthies

A novel approach for selecting appropriate reconstructions is implemented to the hyperbolic conservation laws in the high-order local polynomial-based framework, e.g., the discontinuous Galerkin (DG) and flux reconstruction (FR) schemes.…

Numerical Analysis · Mathematics 2016-05-27 Yoshiaki Abe , Ziyao Sun , Feng Xiao

In this work, we propose a bi-grid scheme framework for the Allen-Cahn equation in Finite Element Method. The new methods are based on the use of two FEM spaces, a coarse one and a fine one, and on a decomposition of the solution into mean…

Numerical Analysis · Mathematics 2017-08-01 Hyam Abboud , Clara Al Kosseifi , Jean-Paul Chehab

In this paper, we present a numerical scheme to solve the initial-boundary value problem for backward stochastic partial differential equations of parabolic type. Based on the Galerkin method, we approximate the original equation by a…

Optimization and Control · Mathematics 2015-07-16 Yanqing Wang

In this paper, we present a conforming discontinuous Galerkin (CDG) finite element method for Brinkman equations. The velocity stabilizer is removed by employing the higher degree polynomials to compute the weak gradient. The theoretical…

Numerical Analysis · Mathematics 2023-03-21 Haoning Dang , Qilong Zhai , Zhongshu Zhao

We propose a novel class of temporal high-order parametric finite element methods for solving a wide range of geometric flows of curves and surfaces. By incorporating the backward differentiation formulae (BDF) for time discretization into…

Numerical Analysis · Mathematics 2024-08-21 Wei Jiang , Chunmei Su , Ganghui Zhang

The commonly used one step methods and linear multi-step methods all have a global error that is of the same order as the local truncation error (as defined in…

Numerical Analysis · Mathematics 2017-01-31 Adi Ditkowski , Sigal Gottlieb

We introduce Discontinuous Galerkin Finite Element Operator Network (DG--FEONet), a data-free operator learning framework that combines the strengths of the discontinuous Galerkin (DG) method with neural networks to solve parametric partial…

Numerical Analysis · Mathematics 2026-01-08 Kapil Chawla , Youngjoon Hong , Jae Yong Lee , Sanghyun Lee

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 focus on the problem of optimal control of large-scale systems whose models are obtained by discretization of partial differential equations using the Finite Element (FE) or Finite Difference (FD) methods. The motivation for studying…

Optimization and Control · Mathematics 2018-06-27 Aleksandar Haber , Michel Verhaegen

In this paper, a non-polynomial spectral Petrov-Galerkin method and associated collocation method for substantial fractional differential equations (FDEs) are proposed, analyzed, and tested. We extend a class of generalized Laguerre…

Numerical Analysis · Mathematics 2014-08-27 Can Huang , Qingshuo Song , Zhimin Zhang

In this paper, we discuss the application of the Generalized Finite Element Method (GFEM) to approximate the solutions of quasilinear elliptic equations with multiple interfaces in one dimensional space. The problem is characterized by…

Numerical Analysis · Mathematics 2021-02-02 Tilsa Aryeni , Quanling Deng , Victor Ginting

We extend the Deep Galerkin Method (DGM) introduced in Sirignano and Spiliopoulos (2018)} to solve a number of partial differential equations (PDEs) that arise in the context of optimal stochastic control and mean field games. First, we…

Computational Finance · Quantitative Finance 2022-04-20 Ali Al-Aradi , Adolfo Correia , Danilo de Frietas Naiff , Gabriel Jardim , Yuri Saporito

The finite element method, finite difference method, finite volume method and spectral method have achieved great success in solving partial differential equations. However, the high accuracy of traditional numerical methods is at the cost…

Numerical Analysis · Mathematics 2020-09-25 Jian Li , Jing Yue , Wen Zhang , Wansuo Duan

The present paper proposes new fully discrete schemes for long-time approximations of stochastic partial differential equations (SPDEs) with non-globally Lipschitz coefficients in a bounded domain $D \subset \R^d, d =1,2,3 $. A novel family…

Numerical Analysis · Mathematics 2026-03-25 Ruisheng Qi , Xiaojie Wang

Large eddy simulation (LES) has been increasingly used to tackle vortex-dominated turbulent flows. In LES, the quality of the simulation results hinges upon the quality of the numerical discretizations in both space and time. It is in this…

Computational Physics · Physics 2018-07-19 Mohammad Alhawwary , Z. J. Wang

We consider the initial/boundary value problem for a diffusion equation involving multiple time-fractional derivatives on a bounded convex polyhedral domain. We analyze a space semidiscrete scheme based on the standard Galerkin finite…

Numerical Analysis · Mathematics 2015-06-18 Bangti Jin , Raytcho Lazarov , Yikan Liu , Zhi Zhou

In this paper, discontinuous Galerkin finite element methods are applied to one dimensional Rosenau equation. Theoretical results including consistency, a priori bounds and optimal error estimates are established for both semidiscrete and…

Numerical Analysis · Mathematics 2019-12-02 P. Danumjaya , K. Balaje

Over the past few years, neural network methods have evolved in various directions for approximating partial differential equations (PDEs). A promising new development is the integration of neural networks with classical numerical…

Numerical Analysis · Mathematics 2025-07-10 Georgios Grekas , Charalambos G. Makridakis , Tristan Pryer
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