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We propose a new class of finite element approximations to ideal compressible magnetohydrodynamic equations in smooth regime. Following variational approximations developed for fluid models in the last decade, our discretizations are built…

Numerical Analysis · Mathematics 2024-02-29 Valentin Carlier , Martin Campos-Pinto

Among the single-trajectory Gaussian-based methods for solving the time-dependent Schr\"{o}dinger equation, the variational Gaussian approximation is the most accurate one. In contrast to Heller's original thawed Gaussian approximation, it…

Quantum Physics · Physics 2024-09-26 Roya Moghaddasi Fereidani , Jiří J. L. Vaníček

We derive a Lagrangian based approach to study the compatible Hamiltonian structure of the dispersionless KdV and supersymmetric KdV hierarchies and claim that our treatment of the problem serves as a very useful supplement of the so-called…

Exactly Solvable and Integrable Systems · Physics 2008-04-25 Amitava Choudhuri , B. Talukdar , U. Das

The general topic of the present paper is to study the conservation for some structural property of a given problem when discretising this problem. Precisely we are interested with Lagrangian or Hamiltonian structures and thus with…

Numerical Analysis · Mathematics 2018-01-17 Jacky Cresson , Isabelle Greff , Charles Pierre

We study the connection between Lagrangian and Hamiltonian descriptions of closed/open dynamics, for a collection of particles with quadratic interaction (closed system) and a sub-collection of particles with linear damping (open system).…

Classical Physics · Physics 2018-09-18 Farhang Haddad Farshi , Fernando Jiménez , Sina Ober-Blöbaum

Recently, a whole class of evergy-preserving integrators has been derived for the numerical solution of Hamiltonian problems. In the mainstream of this research, we have defined a new family of symplectic integrators depending on a real…

Numerical Analysis · Mathematics 2010-09-30 Luigi Brugnano , Felice Iavernaro , Donato Trigiante

This work discusses a variational approach to determining the time evolution operator. We directly see a glimpse of how a generalization of the quantum geometric tensor for unitary operators plays a central role in parameter evolution. We…

Quantum Physics · Physics 2025-04-15 Michael Vogl

In this paper we develop a novel, discrete-time optimal control framework for mechanical systems with uncertain model parameters. We consider finite-horizon problems where the performance index depends on the statistical moments of the…

Optimization and Control · Mathematics 2017-05-17 George I. Boutselis , Yunpeng Pan , Gerardo De La Tore , Evangelos A. Theodorou

In this work, we analyse the discretisation of a recently proposed new Lagrangian approach to optimal control problems of affine-controlled second-order differential equations with cost functions quadratic in the controls. We propose exact…

A variational formulation for accelerated optimization on normed vector spaces was recently introduced in Wibisono et al., and later generalized to the Riemannian manifold setting in Duruisseaux and Leok. This variational framework was…

Numerical Analysis · Mathematics 2022-05-18 Valentin Duruisseaux , Melvin Leok

In this work, the benefits of the phase fitting technique are embedded in high order discrete Lagrangian integrators. The proposed methodology creates integrators with zero phase lag in a test Lagrangian in a similar way used in phase…

Instrumentation and Methods for Astrophysics · Physics 2009-04-02 O. T. Kosmas , D. S. Vlachos

Explicit symplectic integrators have been important tools for accurate and efficient approximations of mechanical systems with separable Hamiltonians. For the first time, the article proposes for arbitrary Hamiltonians similar integrators,…

Numerical Analysis · Mathematics 2016-10-19 Molei Tao

We discuss an elementary derivation of variational symmetries and corresponding integrals of motion for the Lagrangian systems depending on acceleration. Providing several examples, we make the manuscript accessible to a wide range of…

Mathematical Physics · Physics 2023-07-18 Ege Coban , Ilmar Gahramanov , Dilara Kosva

In this paper, we present two Hermite polynomial based approaches to derive one-step numerical integrators for mechanical systems. These methods are based on discretizing the configuration using Hermite polynomials which leads to numerical…

Numerical Analysis · Mathematics 2022-01-20 Harsh Sharma , Mayuresh Patil , Craig Woolsey

We propose a discretization of vector fields that are Hamiltonian up to multiplication by a positive function on the phase space that may be interpreted as a time reparametrization. We prove that our method is structure preserving in the…

Numerical Analysis · Mathematics 2020-08-18 Luis C. García-Naranjo , Mats Vermeeren

Fractional dissipation is a powerful tool to study non-local physical phenomena such as damping models. The design of geometric, in particular, variational integrators for the numerical simulation of such systems relies on a variational…

Numerical Analysis · Mathematics 2024-03-28 Khaled Hariz , Fernando Jiménez , Sina Ober-Blöbaum

Preservation of linear and quadratic invariants by numerical integrators has been well studied. However, many systems have linear or quadratic observables that are not invariant, but which satisfy evolution equations expressing important…

Numerical Analysis · Mathematics 2025-06-02 Robert I. McLachlan , Ari Stern

In this paper we consider the relation between symmetries and first integrals for both continuous canonical Hamiltonian equations and discrete Hamiltonian equations. We observe that canonical Hamiltonian equations can be obtained by…

Mathematical Physics · Physics 2009-06-11 Vladimir Dorodnitsyn , Roman Kozlov

A new approach is developed to integrate numerically the equations of motion for systems of interacting rigid polyatomic molecules. With the aid of a leapfrog framework, we directly involve principal angular velocities into the integration,…

Computational Physics · Physics 2007-05-23 Igor P. Omelyan

Gaussian process regression is increasingly applied for learning unknown dynamical systems. In particular, the implicit quantification of the uncertainty of the learned model makes it a promising approach for safety-critical applications.…

Machine Learning · Computer Science 2022-06-29 Jan Brüdigam , Martin Schuck , Alexandre Capone , Stefan Sosnowski , Sandra Hirche
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