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We study the relationship between local and global error in Runge-Kutta methods for initial-value problems in ordinary differential equations. We show that local error control by means of local extrapolation does not equate to global error…

Numerical Analysis · Mathematics 2015-08-12 J. S. C. Prentice

Next-generation exascale machines with extreme levels of parallelism will provide massive computing resources for large scale numerical simulations of complex physical systems at unprecedented parameter ranges. However, novel numerical…

Computational Physics · Physics 2023-02-08 Komal Kumari , Emmet Cleary , Swapnil Desai , Diego A. Donzis , Jacqueline H. Chen , Konduri Aditya

Semi-discrete Runge-Kutta schemes for nonlinear diffusion equations of parabolic type are analyzed. Conditions are determined under which the schemes dissipate the discrete entropy locally. The dissipation property is a consequence of the…

Numerical Analysis · Mathematics 2015-06-24 Ansgar Jüngel , Stefan Schuchnigg

The time discretization of stochastic spectral fractional wave equation is studied by using the difference methods. Firstly, we exploit rectangle formula to get a low order time discretization, whose the strong convergence order is smaller…

Numerical Analysis · Mathematics 2021-06-08 Xing Liu

Convergence results are shown for full discretizations of quasilinear parabolic partial differential equations on evolving surfaces. As a semidiscretization in space the evolving surface finite element method is considered, using a…

Numerical Analysis · Mathematics 2015-04-01 Balázs Kovács , Christian Andreas Power Guerra

Some continuous optimization methods can be connected to ordinary differential equations (ODEs) by taking continuous limits, and their convergence rates can be explained by the ODEs. However, since such ODEs can achieve any convergence rate…

Numerical Analysis · Mathematics 2022-07-15 Kansei Ushiyama , Shun Sato , Takayasu Matsuo

In this article, we consider combined standard and machine learning methods to solve ODEs and PDEs. We deal with the minimisation problems for the machine learning algorithms and standard discretization methods, which are related to…

Numerical Analysis · Mathematics 2025-08-20 Jürgen Geiser

In practical computation with Runge--Kutta methods, the stage equations are not satisfied exactly, due to roundoff errors, algebraic solver errors, and so forth. We show by example that propagation of such errors within a single step can…

Numerical Analysis · Mathematics 2014-11-25 David I. Ketcheson , Lajos Lóczi , Matteo Parsani

In this paper, the effect that produces the local linearization of the embedded Runge-Kutta formulas of Dormand and Prince for initial value problems is studied. For this, embedded Locally Linearized Runge-Kutta formulas are defined and…

Numerical Analysis · Mathematics 2013-12-24 Juan Carlos Jimenez , Alina Sotolongo , Jose Miguel Sanchez-Bornot

Designing and analyzing optimization methods via continuous-time models expressed as ordinary differential equations (ODEs) is a promising approach for its intuitiveness and simplicity. A key concern, however, is that the convergence rates…

Optimization and Control · Mathematics 2025-12-30 Kansei Ushiyama , Shun Sato , Takayasu Matsuo

The 4-th order Runge-Kutta method in the complex plane is proposed for numerically advancing the solutions of a system of first order differential equations in one external invariant satisfied by the master integrals related to a Feynman…

High Energy Physics - Phenomenology · Physics 2007-05-23 Michele Caffo

Efficient high order numerical methods for evolving the solution of an ordinary differential equation are widely used. The popular Runge--Kutta methods, linear multi-step methods, and more broadly general linear methods, all have a global…

Numerical Analysis · Mathematics 2020-03-16 Adi Ditkowski , Sigal Gottlieb , Zachary J. Grant

Fully implicit Runge-Kutta (IRK) methods have many desirable accuracy and stability properties as time integration schemes, but high-order IRK methods are not commonly used in practice with large-scale numerical PDEs because of the…

Numerical Analysis · Mathematics 2021-10-07 Ben S. Southworth , Oliver Krzysik , Will Pazner

In this paper a set of previous general results for the development of B--series for a broad class of stochastic differential equations has been collected. The applicability of these results is demonstrated by the derivation of B--series…

Numerical Analysis · Mathematics 2025-01-08 Alemayehu Adugna Arara , Kristian Debrabant , Anne Kværnø

We study the convergence of a class of Runge-Kutta type schemes for backward stochastic differential equations (BSDEs) in a Markovian framework. The schemes belonging to the class under consideration benefit from a certain stability…

Probability · Mathematics 2014-03-24 Jean-François Chassagneux , Dan Crisan

Motivated by studies on fully discrete numerical schemes for linear hyperbolic conservation laws, we present a framework on analyzing the strong stability of explicit Runge-Kutta (RK) time discretizations for semi-negative autonomous linear…

Numerical Analysis · Mathematics 2018-11-28 Zheng Sun , Chi-Wang Shu

Differential Riccati equations (DREs) are semilinear matrix- or operator-valued differential equations with quadratic non-linearities. They arise in many different areas, and are particularly important in optimal control of linear quadratic…

Numerical Analysis · Mathematics 2025-04-28 Eskil Hansen , Tony Stillfjord , Teodor Åberg

Instances of discrete quantum systems coupled to a continuum of oscillators are ubiquitous in physics. Often the continua are approximated by a discrete set of modes. We derive analytical error bounds on expectation values of system…

Quantum Physics · Physics 2016-02-16 Mischa P. Woods , Martin B. Plenio

In this paper, exponential Runge-Kutta methods of collocation type (ERKC) which were originally proposed in (Appl Numer Math 53:323-339, 2005) are extended to semilinear parabolic problems with time-dependent delay. Two classes of the ERKC…

Numerical Analysis · Mathematics 2025-12-30 Qiumei Huang , Alexander Ostermann , Gangfan Zhong

We present an adaptive algorithm for effectively solving rough differential equations (RDEs) using the log-ODE method. The algorithm is based on an error representation formula that accurately describes the contribution of local errors to…

Numerical Analysis · Mathematics 2023-07-25 Christian Bayer , Simon Breneis , Terry Lyons