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In this work we propose a new, arbitrary order space-time finite element discretisation for Hamiltonian PDEs in multisymplectic formulation. We show that the new method which is obtained by using both continuous and discontinuous…

Numerical Analysis · Mathematics 2021-08-18 Elena Celledoni , James Jackaman

Partial differential equations (PDEs) describing thermodynamically isolated systems typically possess conserved quantities (like mass, momentum, and energy) and dissipated quantities (like entropy). Preserving these conservation and…

Numerical Analysis · Mathematics 2025-12-01 Boris D. Andrews , Patrick E. Farrell

In this paper, we present and study discontinuous Galerkin (DG) methods for one-dimensional multi-symplectic Hamiltonian partial differential equations. We particularly focus on semi-discrete schemes with spatial discretization only, and…

Numerical Analysis · Mathematics 2020-07-15 Zheng Sun , Yulong Xing

In this paper we propose and investigate a general approach to constructing local energy-preserving algorithms which can be of arbitrarily high order in time for solving Hamiltonian PDEs. This approach is based on the temporal…

Numerical Analysis · Mathematics 2021-03-31 Yuwen Li , Xinyuan Wu

We investigate discretization strategies for a recently introduced class of energy-based models. The model class encompasses classical port-Hamiltonian systems, generalized gradient flows, and certain systems with algebraic constraints. Our…

Numerical Analysis · Mathematics 2026-05-29 Robert Altmann , Attila Karsai , Philipp Schulze

In this paper, we develop a framework to construct energy-preserving methods for multi-components Hamiltonian systems, combining the exponential integrator and the partitioned averaged vector field method. This leads to numerical schemes…

Numerical Analysis · Mathematics 2021-11-08 X. Gu , C. Jiang , Y. Wang , W. Cai

In this paper, we report about recent findings in the numerical solution of Hamiltonian Partial Differential Equations (PDEs), by using energy-conserving line integral methods in the Hamiltonian Boundary Value Methods (HBVMs) class. In…

Numerical Analysis · Mathematics 2019-03-19 Luigi Brugnano , Gianluca Frasca-Caccia , Felice Iavernaro

We introduce a novel numerical method to integrate partial differential equations representing the Hamiltonian dynamics of field theories. It is a multi-symplectic integrator that locally conserves the stress-energy tensor with an excellent…

Numerical Analysis · Mathematics 2017-02-23 Hugo Ricateau , Leticia F. Cugliandolo

In this paper we discuss energy conservation issues related to the numerical solution of the nonlinear wave equation, when a Fourier expansion is considered for the space discretization. The obtained semi-discrete problem is then solved in…

Numerical Analysis · Mathematics 2014-10-28 Luigi Brugnano , Gianluca Frasca Caccia , Felice Iavernaro

For a general class of nonlinear port-Hamiltonian systems we develop a high-order time discretization scheme with certain structure preservation properties. The finite or infinite-dimensional system under consideration possesses a…

Numerical Analysis · Mathematics 2024-07-23 Jan Giesselmann , Attila Karsai , Tabea Tscherpel

In this paper we discuss energy conservation issues related to the numerical solution of the nonlinear wave equation. As is well known, this problem can be cast as a Hamiltonian system that may be autonomous or not, depending on the…

Numerical Analysis · Mathematics 2017-11-27 Luigi Brugnano , Gianluca Frasca Caccia , Felice Iavernaro

We present a novel methodology for constructing arbitrarily high-order structure-preserving methods tailored for damped Hamiltonian systems. This method combines the idea of exponential integrator and energy-preserving collocation methods,…

Numerical Analysis · Mathematics 2024-08-14 Lu Li

This work discusses the model reduction problem for large-scale multi-symplectic PDEs with cubic invariants. For this, we present a linearly implicit global energy-preserving method to construct reduced-order models. This allows to…

Numerical Analysis · Mathematics 2023-08-08 Süleyman Yildiz , Pawan Goyal , Peter Benner

We propose and study a class of arbitrarily high-order numerical discretizations that preserve multiple invariants and are essentially explicit (they do not require the solution of any large systems of algebraic equations). In space, we use…

Numerical Analysis · Mathematics 2026-05-11 Hendrik Ranocha , David I. Ketcheson

In this paper we study the representation of partial differential equations (PDEs) as abstract differential-algebraic equations (DAEs) with dissipative Hamiltonian structure (adHDAEs). We show that these systems not only arise when there…

Functional Analysis · Mathematics 2024-05-20 Volker Mehrmann , Hans Zwart

Energy-preserving numerical methods for solving the Hodge wave equation is developed in this paper. Based on the de Rham complex, the Hodge wave equation can be formulated as a first-order system and mixed finite element methods using…

Numerical Analysis · Mathematics 2020-09-08 Yongke Wu , Yanhong Bai

The energy preserving discrete gradient methods are generalized to finite-dimensional Riemannian manifolds by definition of a discrete approximation to the Riemannian gradient, a retraction, and a coordinate center function. The resulting…

Numerical Analysis · Mathematics 2018-05-22 Elena Celledoni , Sølve Eidnes , Brynjulf Owren , Torbjørn Ringholm

Structure-preserving particle methods have recently been proposed for a class of nonlinear continuity equations, including aggregation-diffusion equation in [J. Carrillo, K. Craig, F. Patacchini, Calc. Var., 58 (2019), pp. 53] and the…

Numerical Analysis · Mathematics 2025-06-19 Jingwei Hu , Samuel Q. Van Fleet , Andy T. S. Wan

Allen--Cahn equation with constant and degenerate mobility, and with polynomial and logarithmic energy functionals is discretized using symmetric interior penalty discontinuous Galerkin (SIPG) finite elements in space. We show that the…

Numerical Analysis · Mathematics 2015-05-19 Bülent Karasözen , Ayşe Sarıaydın Filibelioğlu , Murat Uzunca

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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