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We introduce new methods for the numerical solution of general Hamiltonian boundary value problems. The main feature of the new formulae is to produce numerical solutions along which the energy is precisely conserved, as is the case with…

Numerical Analysis · Mathematics 2014-11-26 P. Amodio , L. Brugnano , F. Iavernaro

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

In this paper, a class of high-order methods to numerically solve Functional Differential Equations with Piecewise Continuous Arguments (FDEPCAs) is discussed. The framework stems from the expansion of the vector field associated with the…

Numerical Analysis · Mathematics 2024-03-14 Gianmarco Gurioli , Weijie Wang , Xiaoqiang Yan

We study geometric properties of Krylov projection methods for large and sparse linear Hamiltonian systems. We consider in particular energy preservation. We discuss the connection to structure preserving model reduction. We illustrate the…

Numerical Analysis · Mathematics 2018-08-22 Elena Celledoni , Lu Li

The systems with small binding energies and widely distributed in space bound-state wave functions are considered. Because the interaction potential is weak and rather localized compared to the characteristic sizes of wave functions of…

Atomic Physics · Physics 2021-07-07 V. A. Timoshenko , E. A. Yarevsky

For Hamiltonian systems, simulation algorithms that exactly conserve numerical energy or pseudo-energy have seen extensive investigation. Most available methods either require the iterative solution of nonlinear algebraic equations at each…

Numerical Analysis · Mathematics 2022-07-04 Stefan Bilbao , Michele Ducceschi , Fabiana Zama

Structure-preserving algorithms and algorithms with uniform error bound have constituted two interesting classes of numerical methods. In this paper, we blend these two kinds of methods for solving nonlinear Hamiltonian systems with highly…

Numerical Analysis · Mathematics 2021-02-08 Bin Wang , Yaolin Jiang

In this paper, we define arbitrarily high-order energy-conserving methods for Hamiltonian systems with quadratic holonomic constraints. The derivation of the methods is made within the so-called line integral framework. Numerical tests to…

Numerical Analysis · Mathematics 2024-04-11 P. Amodio , L. Brugnano , G. Frasca-Caccia , F. Iavernaro

As is well known, energy is generally deemed as one of the most important physical invariants in many conservative problems and hence it is of remarkable interest to consider numerical methods which are able to preserve it. In this paper,…

Numerical Analysis · Mathematics 2025-07-23 Wensheng Tang

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

The primary challenge in solving kinetic equations, such as the Vlasov equation, is the high-dimensional phase space. In this context, dynamical low-rank approximations have emerged as a promising way to reduce the high computational cost…

Numerical Analysis · Mathematics 2021-07-28 Lukas Einkemmer , Ilon Joseph

High order energy-preserving methods for Hamiltonian systems are presented. For this aim, an energy-preserving condition of continuous stage Runge--Kutta methods is proved. Order conditions are simplified and parallelizable conditions are…

Numerical Analysis · Mathematics 2016-11-08 Yuto Miyatake , John C. Butcher

Kahan's method and a two-step generalization of the discrete gradient method are both linearly implicit methods that can preserve a modified energy for Hamiltonian systems with a cubic Hamiltonian. These methods are here investigated and…

Numerical Analysis · Mathematics 2020-05-11 Sølve Eidnes , Lu Li , Shun Sato

This paper aims at developing exactly energy-conservative and structure-preserving finite volume schemes for the discretisation of first-order symmetric-hyperbolic and thermodynamically compatible (SHTC) systems of partial differential…

Numerical Analysis · Mathematics 2026-01-01 Alessia Lucca , Michael Dumbser

Score matching (SM) provides a compelling approach to learn energy-based models (EBMs) by avoiding the calculation of partition function. However, it remains largely open to learn energy-based latent variable models (EBLVMs), except some…

Machine Learning · Computer Science 2020-10-19 Fan Bao , Chongxuan Li , Kun Xu , Hang Su , Jun Zhu , Bo Zhang

In this paper, we develop Hamiltonian particle-in-cell methods for Vlasov-Maxwell equations by applying conforming finite element methods in space and splitting methods in time. For the spatial discretisation, the criteria for choosing…

Computational Physics · Physics 2016-10-12 Yang He , Yajuan Sun , Hong Qin , Jian Liu

In this paper, we present a novel strategy to systematically construct linearly implicit energy-preserving schemes with arbitrary order of accuracy for Hamiltonian PDEs. Such novel strategy is based on the newly developed exponential scalar…

Numerical Analysis · Mathematics 2023-07-27 Yonghui Bo , Yushun Wang , Wenjun Cai

This work proposes an adaptive structure-preserving model order reduction method for finite-dimensional parametrized Hamiltonian systems modeling non-dissipative phenomena. To overcome the slowly decaying Kolmogorov width typical of…

Numerical Analysis · Mathematics 2022-02-02 Jan S. Hesthaven , Cecilia Pagliantini , Nicolò Ripamonti

We present an energy-conserving numerical scheme to solve the Vlasov-Maxwell (VM) system based on the regularized moment method proposed in [Z. Cai, Y. Fan, and R. Li. CPAM, 2014]. The globally hyperbolic moment system is deduced for the…

Numerical Analysis · Mathematics 2023-01-25 Tianai Yin , Xinghui Zhong , Yanli Wang

Model order reduction provides low-complexity high-fidelity surrogate models that allow rapid and accurate solutions of parametric differential equations. The development of reduced order models for parametric \emph{nonlinear} Hamiltonian…

Numerical Analysis · Mathematics 2024-09-30 Cecilia Pagliantini , Federico Vismara