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The Galerkin difference (GD) basis is a set of continuous, piecewise polynomials defined using a finite difference like grid of degrees of freedom. The one dimensional GD basis functions are naturally extended to multiple dimensions using…

Numerical Analysis · Mathematics 2021-06-03 Jeremy E. Kozdon , Lucas C. Wilcox , Thomas Hagstrom , Jeffrey W. Banks

High-order partial differential equations (PDEs) require derivative regularity that standard $C^0$ finite element infrastructures do not directly provide on unstructured meshes. We propose a mesh-intrinsic generalized finite element method…

Numerical Analysis · Mathematics 2026-04-28 Rong Tian

In this work, we propose and investigate stable high-order collocation-type discretisations of the discontinuous Galerkin method on equidistant and scattered collocation points. We do so by incorporating the concept of discrete least…

Numerical Analysis · Mathematics 2021-02-24 Jan Glaubitz , Philipp Oeffner

We present a new $hp$-version space-time discontinuous Galerkin (dG) finite element method for the numerical approximation of parabolic evolution equations on general spatial meshes consisting of polygonal/polyhedral (polytopic) elements,…

Numerical Analysis · Mathematics 2024-11-07 Andrea Cangiani , Zhaonan Dong , Emmanuil H. Georgoulis

In this work, we propose a novel formulation for the solution of partial differential equations using finite element methods on unfitted meshes. The proposed formulation relies on the discrete extension operator proposed in the aggregated…

Numerical Analysis · Mathematics 2022-08-15 Santiago Badia , Eric Neiva , Francesc Verdugo

We introduce a nodally bound-preserving Galerkin method for second-order elliptic problems on general polygonal/polyhedral, henceforth collectively termed as \emph{polytopic}, meshes. Starting from an interior penalty discontinuous Galerkin…

Numerical Analysis · Mathematics 2025-10-03 Abdolreza Amiri , Gabriel R. Barrenechea , Emmanuil H. Georgoulis , Tristan Pryer

We develop a hybrid spatial discretization for the wave equation in second order form, based on high-order accurate finite difference methods and discontinuous Galerkin methods. The hybridization combines computational efficiency of finite…

Numerical Analysis · Mathematics 2022-10-26 Siyang Wang , Gunilla Kreiss

In a recent work [10], we have introduced a pressure-robust Hybrid High-Order method for the numerical solution of the incompressible Navier-Stokes equations on matching simplicial meshes. Pressure-robust methods are characterized by error…

Numerical Analysis · Mathematics 2026-01-22 Daniel Castanon Quiroz , Daniele A. Di Pietro

This paper introduces a novel staggered discontinuous Galerkin (SDG) method tailored for solving elliptic equations on polytopal meshes. Our approach utilizes a primal-dual grid framework to ensure local conservation of fluxes,…

Numerical Analysis · Mathematics 2024-11-01 L. Chen , X. Huang , E. Park , R. Wang

In this paper, we develop a family of high order cut discontinuous Galerkin (DG) methods for hyperbolic conservation laws in one space dimension. The ghost penalty stabilization is used to stabilize the scheme for small cut elements. The…

Numerical Analysis · Mathematics 2021-04-13 Pei Fu , Gunilla Kreiss

In this paper, we investigate a general class of stochastic gradient descent (SGD) algorithms, called Conditioned SGD, based on a preconditioning of the gradient direction. Using a discrete-time approach with martingale tools, we establish…

Statistics Theory · Mathematics 2023-10-17 Rémi Leluc , François Portier

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

The stability and generalization of stochastic gradient-based methods provide valuable insights into understanding the algorithmic performance of machine learning models. As the main workhorse for deep learning, stochastic gradient descent…

Machine Learning · Statistics 2021-02-24 Tao Sun , Dongsheng Li , Bao Wang

A conforming discontinuous Galerkin finite element method is introduced for solving the biharmonic equation. This method, by its name, uses discontinuous approximations and keeps simple formulation of the conforming finite element method at…

Numerical Analysis · Mathematics 2019-07-26 Xiu Ye , Shangyou Zhang

Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose and discuss a novel approach to scale up SGD in applications involving non-convex functions…

Machine Learning · Statistics 2022-10-07 Saad Mohamad , Hamad Alamri , Abdelhamid Bouchachia

We design and analyze an approximation method for advection-diffusion-reaction equations where the (generalized) degrees of freedom are polynomials of order $k\ge0$ at mesh faces. The method hinges on local discrete reconstruction operators…

Numerical Analysis · Mathematics 2018-05-29 Daniele A. Di Pietro , Jérôme Droniou , Alexandre Ern

We present a novel high-order accurate nodal discontinuous Galerkin (DG) method for solving nonlinear hyperbolic systems of partial differential equations (PDEs) on fully unstructured three-dimensional polyhedral meshes. A mesh generator is…

Numerical Analysis · Mathematics 2026-05-04 Sixtine Michel , Lorenzo Diazzi , Walter Boscheri

We present a structure-preserving scheme based on a recently-proposed mixed formulation for incompressible hyperelasticity formulated in principal stretches. Although there exist Hamiltonians introduced for quasi-incompressible…

Numerical Analysis · Mathematics 2023-06-28 Jiashen Guan , Hongyan Yuan , Ju Liu

We introduce an $hp$-version symmetric interior penalty discontinuous Galerkin finite element method (DGFEM) for the numerical approximation of the biharmonic equation on general computational meshes consisting of polygonal/polyhedral…

Numerical Analysis · Mathematics 2018-09-25 Zhaonan Dong

The training of machine learning models is typically carried out using some form of gradient descent, often with great success. However, non-asymptotic analyses of first-order optimization algorithms typically employ a gradient smoothness…

Machine Learning · Computer Science 2024-06-18 Thomas Flynn