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In this paper we present a general procedure for designing higher strong order methods for It\^o stochastic differential equations on matrix Lie groups and illustrate this strategy with two novel schemes that have a strong convergence order…

Numerical Analysis · Mathematics 2021-02-09 Michelle Muniz , Matthias Ehrhardt , Michael Günther , Renate Winkler

We study the application of the generalized convolution quadrature (gCQ) based on Runge--Kutta methods to approximate the solution of an important class of sectorial problems. The gCQ generalizes Lubich's original convolution quadrature…

Numerical Analysis · Mathematics 2025-06-27 Jing Guo , Maria Lopez-Fernandez

We present unconditionally energy stable Runge-Kutta (RK) discontinuous Galerkin (DG) schemes for solving a class of fourth order gradient flows. Our algorithm is geared toward arbitrarily high order approximations in both space and time,…

Numerical Analysis · Mathematics 2021-01-05 Hailiang Liu , Peimeng Yin

The existing discrete variational derivative method is only second-order accurate and fully implicit. In this paper, we propose a framework to construct an arbitrary high-order implicit (original) energy stable scheme and a second-order…

Numerical Analysis · Mathematics 2022-10-24 Jizu Huang

In current research, we analyse dissipation and dispersion characteristics of most accurate two and three stage Gauss-Legendre implicit Runge-Kutta (R-K) methods. These methods, known for their $A$-stability and immense accuracy, are…

Numerical Analysis · Mathematics 2019-06-25 Subhajit Giri , Shuvam Sen

An error analysis is presented for explicit partitioned Runge-Kutta methods and multirate methods applied to conservation laws. The interfaces, across which different methods or time steps are used, lead to order reduction of the schemes.…

Numerical Analysis · Mathematics 2013-10-29 Willem Hundsdorfer , David I. Ketcheson , Igor Savostianov

This paper deals with stability of classical Runge-Kutta collocation methods. When such methods are embedded in linearly implicit methods as developed in [12] and used in [13] for the time integration of nonlinear evolution PDEs, the…

Numerical Analysis · Mathematics 2023-04-20 Guillaume Dujardin , Ingrid Lacroix-Violet

We note a fact that stiff systems or differential equations that have highly oscillatory solutions cannot be solved efficiently using conventional methods. In this paper, we study two new classes of exponential Runge-Kutta (ERK) integrators…

Numerical Analysis · Mathematics 2023-12-06 Bin Wang , Xianfa Hu , Xinyuan Wu

In this paper, we develop a higher order symmetric partitioned Runge-Kutta method for a coupled system of differential equations on Lie groups. We start with a discussion on partitioned Runge-Kutta methods on Lie groups of arbitrary order.…

High Energy Physics - Lattice · Physics 2011-09-15 Michèle Wandelt , Michael Günther , Francesco Knechtli , Michael Striebel

In this paper, we study the uniform accuracy of implicit-explicit (IMEX) Runge-Kutta (RK) schemes for general linear hyperbolic relaxation systems satisfying the structural stability condition proposed in \cite{yong_singular_1999}. We…

Numerical Analysis · Mathematics 2025-06-27 Zhiting Ma , Juntao Huang

A wide range of physical phenomena exhibit auxiliary admissibility criteria, such as conservation of entropy or various energies, which arise implicitly under exact solution of their governing PDEs. However, standard temporal schemes, such…

Numerical Analysis · Mathematics 2025-03-27 Mohammad R. Najafian , Brian C. Vermeire

This work proposes and analyzes a new class of numerical integrators for computing low-rank approximations to solutions of matrix differential equation. We combine an explicit Runge-Kutta method with repeated randomized low-rank…

Numerical Analysis · Mathematics 2024-09-11 Hei Yin Lam , Gianluca Ceruti , Daniel Kressner

Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper…

Numerical Analysis · Mathematics 2026-02-11 Steven Roberts , Andrey A Popov , Arash Sarshar , Adrian Sandu

In this paper, we propose Galerkin-Legendre spectral method with implicit Runge-Kutta method for solving the unsteady two-dimensional Schrodinger equation with nonhomogeneous Dirichlet boundary conditions and initial condition. We apply a…

Numerical Analysis · Mathematics 2018-11-12 Wenjie Liu , Boying Wu

This work constructs the first-ever sixth-order exponential Runge--Kutta (ExpRK) methods for the time integration of stiff parabolic PDEs. First, we leverage the exponential B-series theory to restate the stiff order conditions for ExpRK…

Numerical Analysis · Mathematics 2024-02-28 Vu Thai Luan , Trky Alhsmy

In this paper, we develop a high order finite difference boundary treatment method for the implicit-explicit (IMEX) Runge-Kutta (RK) schemes solving hyperbolic systems with possibly stiff source terms on a Cartesian mesh. The main challenge…

Numerical Analysis · Mathematics 2020-10-28 Weifeng Zhao , Juntao Huang

The result after $N$ steps of an implicit Runge-Kutta time discretization of an inhomogeneous linear parabolic differential equation is computed, up to accuracy $\epsilon$, by solving only $$O\Big(\log N \log \frac1\epsilon \Big) $$ linear…

Numerical Analysis · Mathematics 2011-11-10 María López-Fernández , Christian Lubich , Cesar Palencia , Achim Schädle

In this work modified Patankar-Runge-Kutta (MPRK) schemes up to order four are considered and equipped with a dense output formula of appropriate accuracy. Since these time integrators are conservative and positivity preserving for any time…

Numerical Analysis · Mathematics 2025-01-24 Thomas Izgin

We propose a new probabilistic scheme which combines deep learning techniques with high order schemes for backward stochastic differential equations belonging to the class of Runge-Kutta methods to solve high-dimensional semi-linear…

Numerical Analysis · Mathematics 2023-01-02 Jean-François Chassagneux , Junchao Chen , Noufel Frikha

Statistical regression models whose mean functions are represented by ordinary differential equations (ODEs) can be used to describe phenomenons dynamical in nature, which are abundant in areas such as biology, climatology and genetics. The…

Methodology · Statistics 2017-05-15 Kyoungjae Lee , Jaeyong Lee , Sarat C. Dass