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In this paper, we develop a structure-preserving discretization of the Lagrangian framework for electromagnetism, combining techniques from variational integrators and discrete differential forms. This leads to a general family of…

Numerical Analysis · Mathematics 2015-11-05 Ari Stern , Yiying Tong , Mathieu Desbrun , Jerrold E. Marsden

This article considers Hamiltonian mechanical systems with potential functions admitting jump discontinuities. The focus is on accurate and efficient numerical approximations of their solutions, which will be defined via the laws of…

Numerical Analysis · Mathematics 2022-01-05 Molei Tao , Shi Jin

In this paper, we introduce a fourth-order accurate finite element method for incompressible variable density flow. The method is implicit in time and constructed with the Taylor series technique, and uses standard high-order Lagrange basis…

Numerical Analysis · Mathematics 2022-09-21 Lukas Lundgren , Murtazo Nazarov

Standard numerical integrators suffer from an order reduction when applied to nonlinear Schr\"{o}dinger equations with low-regularity initial data. For example, standard Strang splitting requires the boundedness of the solution in $H^{r+4}$…

Numerical Analysis · Mathematics 2019-06-04 Marvin Knöller , Alexander Ostermann , Katharina Schratz

We consider time discretizations of the two-dimensional Euler equation written in vorticity form. The discretization method uses a Crouch-Grossman integrator that proceeds in two stages: first freezing the velocity vector field at the…

Analysis of PDEs · Mathematics 2018-11-06 Romain Horsin

We study a fully discrete finite element method for variable-order time-fractional diffusion equations with a time-dependent variable order. Optimal convergence estimates are proved with the first-order accuracy in time (and second order…

Numerical Analysis · Mathematics 2019-05-15 Xiangcheng Zheng , Fanhai Zeng , Hong Wang

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

In recent publications, the construction of explicit symplectic integrators for Schwarzschild and Kerr type spacetimes is based on splitting and composition methods for numerical integrations of Hamiltonians or time-transformed Hamiltonians…

General Relativity and Quantum Cosmology · Physics 2022-03-30 Naying Zhou , Hongxing Zhang , Wenfang Liu , Xin Wu

A consequent approach is proposed to construct symplectic force-gradient algorithms of arbitrarily high orders in the time step for precise integration of motion in classical and quantum mechanics simulations. Within this approach the basic…

Statistical Mechanics · Physics 2009-11-07 Igor Omelyan , Ihor Mryglod , Reinhard Folk

The formulation of the time-dependent Schrodinger equation in terms of coupled-cluster theory is outlined, with emphasis on the bivariational framework and its classical Hamiltonian structure. An indefinite inner product is introduced,…

Chemical Physics · Physics 2019-04-16 Thomas Bondo Pedersen , Simen Kvaal

By combining a standard symmetric, symplectic integrator with a new step size controller, we provide an integration scheme that is symmetric, reversible and conserves the values of the constants of motion. This new scheme is appropriate for…

General Relativity and Quantum Cosmology · Physics 2012-12-07 Jonathan Seyrich , Georgios Lukes-Gerakopoulos

Symplectic integration algorithms have become popular in recent years in long-term orbital integrations because these algorithms enforce certain conservation laws that are intrinsic to Hamiltonian systems. For problems with large variations…

Astrophysics · Physics 2007-05-23 Man Hoi Lee , Martin J. Duncan , Harold F. Levison

In this work, new finite difference schemes are presented for dealing with the upper-convected time derivative in the context of the generalized Lie derivative. The upper-convected time derivative, which is usually encountered in the…

Numerical Analysis · Mathematics 2023-03-31 Debora O. Medeiros , Hirofumi Notsu , Cassio M. Oishi

This study investigates numerical methods to solve nonlinear transport problems characterized by various sorption isotherms with a focus on the Freundlich type of isotherms. We describe and compare second order accurate numerical schemes,…

Numerical Analysis · Mathematics 2025-07-22 Dagmar Zakova , Peter Frolkovic

Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference especially in high dimensional and large data settings. To control the computational cost while being able to capture…

Machine Learning · Computer Science 2021-04-07 Bingxin Zhou , Junbin Gao , Minh-Ngoc Tran , Richard Gerlach

The explicit split-operator algorithm is often used for solving the linear and nonlinear time-dependent Schr\"{o}dinger equations. However, when applied to certain nonlinear time-dependent Schr\"{o}dinger equations, this algorithm loses…

Chemical Physics · Physics 2024-09-26 Julien Roulet , Jiří Vaníček

We develop structure-preserving time integration schemes for Gaussian wave packet dynamics associated with the magnetic Schr\"odinger equation. The variational Dirac--Frenkel formulation yields a finite-dimensional Hamiltonian system for…

Numerical Analysis · Mathematics 2026-03-27 Sebastian Merk , Caroline Lasser

Symplectic integration methods based on operator splitting are well established in many branches of science. For Hamiltonian systems which split in more than two parts, symplectic methods of higher order have been studied in detail only for…

Finite element methods provide accurate and efficient methods for the numerical solution of partial differential equations by means of restricting variational problems to finite-dimensional approximating spaces. However, they do not…

Numerical Analysis · Mathematics 2025-06-24 Robert C. Kirby , John D. Stephens

Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a target distribution - either the posterior or an augmented…

Computation · Statistics 2019-11-21 Michael Stanley Smith , Ruben Loaiza-Maya , David J. Nott
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