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We introduce generalized Galerkin variational integrators, which are a natural generalization of discrete variational mechanics, whereby the discrete action, as opposed to the discrete Lagrangian, is the fundamental object. This is achieved…

Numerical Analysis · Mathematics 2007-05-23 Melvin Leok

In this paper, we present a new variational integrator for problems in Lagrangian mechanics. Using techniques from Galerkin variational integrators, we construct a scheme for numerical integration that converges geometrically, and is…

Numerical Analysis · Mathematics 2012-11-20 James Hall , Melvin Leok

In this work we derive and analyze variational integrators of higher order for the structure-preserving simulation of mechanical systems. The construction is based on a space of polynomials together with Gauss and Lobatto quadrature rules…

Numerical Analysis · Mathematics 2014-04-08 Sina Ober-Blöbaum , Nils Saake

This article presents a superconvergence for the gradient approximation of the second order elliptic equation discretized by the weak Galerkin finite element methods on nonuniform rectangular partitions. The result shows a convergence of…

Numerical Analysis · Mathematics 2018-06-21 Dan Li , Chunmei Wang , Junping Wang

A superconvergence error estimate for the gradient approximation of the second order elliptic problem in three dimensions is analyzed by using weak Galerkin finite element scheme on the uniform and non-uniform cubic partitions. Due to the…

Numerical Analysis · Mathematics 2018-10-19 Dan Li , Yufeng Nie , Chunmei Wang

We reconsider the variational derivation of symplectic partitioned Runge-Kutta schemes. Such type of variational integrators are of great importance since they integrate mechanical systems with high order accuracy while preserving the…

Numerical Analysis · Mathematics 2015-05-08 Cédric M. Campos

We study a second order hyperbolic initial-boundary value partial differential equation with memory, that results in an integro-differential equation with a convolution kernel. The kernel is assumed to be either smooth or no worse than…

Numerical Analysis · Mathematics 2014-01-29 Fardin Saedpanah

We introduce a novel technique for constructing higher-order variational integrators for Hamiltonian systems of ODEs. In particular, we are concerned with generating globally smooth approximations to solutions of a Hamiltonian system. Our…

Numerical Analysis · Mathematics 2015-03-17 Melvin Leok , Tatiana Shingel

The numerical analysis of variational integrators relies on variational error analysis, which relates the order of accuracy of a variational integrator with the order of approximation of the exact discrete Lagrangian by a computable…

Numerical Analysis · Mathematics 2011-02-15 Melvin Leok , Tatiana Shingel

We present a new approach to using neural networks to approximate the solutions of variational equations, based on the adaptive construction of a sequence of finite-dimensional subspaces whose basis functions are realizations of a sequence…

Machine Learning · Computer Science 2021-06-01 Mark Ainsworth , Justin Dong

The elucidation of many physical problems in science and engineering is subject to the accurate numerical modelling of complex wave propagation phenomena. Over the last decades, high-order numerical approximation for partial differential…

Numerical Analysis · Mathematics 2025-10-20 Mathias Anselmann , Markus Bause

The conforming finite element Galerkin method is applied to discretise in the spatial direction for a class of strongly nonlinear parabolic problems. Using elliptic projection of the associated linearised stationary problem with Gronwall…

Numerical Analysis · Mathematics 2021-08-04 Ambit Kumar Pany , Morrakot Khebchareon , Amiya K. Pani

The discontinuous Galerkin dG method provides a robust and flexible technique for the time integration of fractional diffusion problems. However, a practical implementation uses coefficients defined by integrals that are not easily…

Numerical Analysis · Mathematics 2022-08-09 William McLean

We introduce and analyze a class of Galerkin-collocation discretization schemes in time for the wave equation. Its conceptual basis is the establishment of a direct connection between the Galerkin method for the time discretization and the…

Numerical Analysis · Mathematics 2019-08-23 Mathias Anselmann , Markus Bause , Simon Becher , Gunar Matthies

In this paper we investigate numerically the order of convergence of an isogeometric collocation method that builds upon the least-squares collocation method presented in [1] and the variational collocation method presented in [2]. The…

Numerical Analysis · Mathematics 2017-04-05 Monica Montardini , Giancarlo Sangalli , Lorenzo Tamellini

This paper studies a new class of integration schemes for the numerical solution of semi-explicit differential-algebraic equations of differentiation index 2 in Hessenberg form. Our schemes provide the flexibility to choose different…

Numerical Analysis · Mathematics 2021-04-14 Robert Altmann , Roland Herzog

In this paper we study the numerical method for approximating the random periodic solution of semiliear stochastic evolution equations. The main challenge lies in proving a convergence over an infinite time horizon while simulating…

Probability · Mathematics 2022-05-12 Yue Wu , Chenggui Yuan

We consider a Urysohn integral operator $\mathcal{K}$ with kernel of the type of Green's function. For $r \geq 1$, a space of piecewise polynomials of degree $\leq r-1 $ with respect to a uniform partition is chosen to be the approximating…

Numerical Analysis · Mathematics 2021-10-26 Gobinda Rakshit , Akshay S. Rane , Kshitij Patil

We introduce a family of proximal discontinuous Galerkin methods for variational inequalities, focusing on the obstacle problem as a didactic example. Each member of this family is born from applying a different well-known nonconforming…

Numerical Analysis · Mathematics 2026-04-23 Alexandre Ern , Brendan Keith , Dohyun Kim , Rami Masri , Beatrice Riviere

Complementarity problems and variational inequalities arise in a wide variety of areas, including machine learning, planning, game theory, and physical simulation. In all of these areas, to handle large-scale problem instances, we need fast…

Machine Learning · Computer Science 2013-06-21 Geoffrey J. Gordon
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