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We address the problem of constructing numerical integrators for nonholonomic Lagrangian systems that enjoy appropriate discrete versions of the geometric properties of the continuous flow, including the preservation of energy. Building on…

Numerical Analysis · Mathematics 2025-10-20 Jorge Cortes

In this paper, we design a novel class of arbitrarily high-order structure-preserving numerical schemes for the time-dependent Gross-Pitaevskii equation with angular momentum rotation in three dimensions. Based on the idea of the scalar…

Numerical Analysis · Mathematics 2021-02-03 Jin Cui , Yushun Wang , Chaolong Jiang

We examine interpolatory model reduction methods that are well-suited for treating large scale port-Hamiltonian differential-algebraic systems in a way that is able to preserve and indeed, take advantage of the underlying structural…

Numerical Analysis · Mathematics 2021-11-03 Chris A. Beattie , Serkan Gugercin , Volker Mehrmann

Since the expense of the numerical integration of large scale dynamical systems is often computationally prohibitive, model reduction methods, which approximate such systems by simpler and much lower order ones, are often employed to reduce…

Numerical Analysis · Mathematics 2018-10-25 Yuto Miyatake

We establish sharp energy decay rates for a large class of nonlinearly first-order damped systems, and we design discretization schemes that inherit of the same energy decay rates, uniformly with respect to the space and/or time…

Analysis of PDEs · Mathematics 2015-12-17 Fatiha Alabau-Boussouira , Yannick Privat , Emmanuel Trélat

Hamiltonian Boundary Value Methods are a new class of energy preserving one step methods for the solution of polynomial Hamiltonian dynamical systems. They can be thought of as a generalization of collocation methods in that they may be…

Numerical Analysis · Mathematics 2010-11-04 Luigi Brugnano , Felice Iavernaro , Tiziana Susca

This paper presents a structure-preserving model reduction approach applicable to large-scale, nonlinear port-Hamiltonian systems. Structure preservation in the reduction step ensures the retention of port-Hamiltonian structure which, in…

Numerical Analysis · Mathematics 2016-01-05 Saifon Chaturantabut , Chris Beattie , Serkan Gugercin

In this paper an approach is outlined. With this approach some explicit algorithms can be applied to solve the initial value problem of $n-$dimensional damped oscillators. This approach is based upon following structure: for any…

Mathematical Physics · Physics 2011-03-09 Tianshu Luo , Yimu Guo

In this paper we design discrete port-Hamiltonian systems systematically in two different ways, by applying discrete gradient methods and splitting methods respectively. The discrete port-Hamiltonian systems we get satisfy a discrete notion…

Numerical Analysis · Mathematics 2017-06-28 Elena Celledoni , Eirik Hoel Høiseth

This paper considers structure-preserving model order reduction (MOR) techniques for port-Hamiltonian (pH) systems, which are typically derived from energy-based modelling. To keep favorable properties of pH systems such as stability and…

Numerical Analysis · Mathematics 2026-03-10 Silke Glas , Hongliang Mu

We study the structure-preserving space discretization of port-Hamiltonian (pH) systems defined with differential constitutive relations. Using the concept of Stokes-Lagrange structure to describe these relations, these are reduced to a…

Numerical Analysis · Mathematics 2026-04-14 Antoine Bendimerad-Hohl , Ghislain Haine , Laurent Lefèvre , Denis Matignon

We present a novel structure-preserving framework for solving the Vlasov-Poisson-Landau system of equations using a particle in cell (PIC) discretization combined with discrete gradient time integrators. The Vlasov-Poisson-Landau system is…

Plasma Physics · Physics 2026-02-16 Daniel S. Finn , Joseph V. Pusztay , Matthew G. Knepley , Mark F. Adams

We use the general framework of summation-by-parts operators to construct conservative, energy-stable, and well-balanced semidiscretizations of two different nonlinear systems of dispersive shallow water equations with varying bathymetry:…

Numerical Analysis · Mathematics 2025-11-12 Joshua Lampert , Hendrik Ranocha

One main issue, when numerically integrating autonomous Hamiltonian systems, is the long-term conservation of some of its invariants, among which the Hamiltonian function itself. For example, it is well known that classical symplectic…

Numerical Analysis · Mathematics 2014-06-23 Luigi Brugnano , Felice Iavernaro , Donato Trigiante

An energy preserving reduced order model is developed for two dimensional nonlinear Schr\"odinger equation (NLSE) with plane wave solutions and with an external potential. The NLSE is discretized in space by the symmetric interior penalty…

Numerical Analysis · Mathematics 2019-01-15 Bülent Karasözen , Murat Uzunca

In this paper, we construct high order energy dissipative and conservative local discontinuous Galerkin methods for the Fornberg-Whitham type equations. We give the proofs for the dissipation and conservation for related conservative…

Numerical Analysis · Mathematics 2021-06-09 Qian Zhang , Yan Xu , Chi-Wang Shu

This work proposes a model-reduction methodology that preserves Lagrangian structure (equivalently Hamiltonian structure) and achieves computational efficiency in the presence of high-order nonlinearities and arbitrary parameter dependence.…

Computational Engineering, Finance, and Science · Computer Science 2015-04-16 Kevin Carlberg , Ray Tuminaro , Paul Boggs

In this paper, we present continuous-stage partitioned Runge-Kutta (csPRK) methods for energy-preserving integration of Hamiltonian systems. A sufficient condition for the energy preservation of the csPRK methods is derived. It is shown…

Numerical Analysis · Mathematics 2025-07-25 Wensheng Tang

In the framework of a mixed finite element method, a structure-preserving formulation for incompressible magnetohydrodynamic (MHD) equations with general boundary conditions is proposed. A leapfrog-type temporal scheme fully decouples the…

Numerical Analysis · Mathematics 2025-05-20 Yi Zhang , Artur Palha , Andrea Brugnoli , Deepesh Toshniwal , Marc Gerritsma

This paper presents an energy-preserving machine learning method for inferring reduced-order models (ROMs) by exploiting the multi-symplectic form of partial differential equations (PDEs). The vast majority of energy-preserving…

Machine Learning · Computer Science 2024-09-17 Süleyman Yıldız , Pawan Goyal , Peter Benner
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