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This work deals with two groups of spectral analysis results for matrices arising in fully implicit Runge-Kutta methods used for linear time-dependent partial differential equations. These were applied for different formulations of the same…

Numerical Analysis · Mathematics 2025-10-27 Michal Outrata

The integration of gyroscope measurements is an essential task for most navigation systems. Modern vehicles typically use strapdown systems, such that gyro integration requires coning compensation to account for the sensor's rotation during…

Robotics · Computer Science 2026-03-06 John A. Christian , Michael R. Walker , Wyatt Bridgman , Michael J. Sparapany

In this paper we consider time-dependent PDEs discretized by a special class of Physics Informed Neural Networks whose design is based on the framework of Runge--Kutta and related time-Galerkin discretizations. The primary motivation for…

Numerical Analysis · Mathematics 2026-02-10 Georgios Akrivis , Charalambos G. Makridakis , Costas Smaragdakis

An error analysis of Runge-Kutta convolution quadrature based on Gauss methods applied to hyperbolic operators is given. The order of convergence relies heavily on the parity of the number of stages, a more favourable situation arising for…

Numerical Analysis · Mathematics 2022-12-15 Lehel Banjai , Matteo Ferrari

In this paper we define an efficient implementation of Runge-Kutta methods of Radau IIA type, which are commonly used when solving stiff ODE-IVPs problems. The proposed implementation relies on an alternative low-rank formulation of the…

Numerical Analysis · Mathematics 2024-07-18 L. Brugnano , F. Iavernaro , C. Magherini

The aim of this paper is to construct and analyze explicit exponential Runge-Kutta methods for the temporal discretization of linear and semilinear integro-differential equations. By expanding the errors of the numerical method in terms of…

Numerical Analysis · Mathematics 2023-01-24 Alexander Ostermann , Fardin Saedpanah , Nasrin Vaisi

In this paper we discuss a framework for the polynomial approximation to the solution of initial value problems for differential equations. The framework, initially devised for the approximation of ordinary differential equations, is…

Numerical Analysis · Mathematics 2022-11-15 Luigi Brugnano , Gianluca Frasca-Caccia , Felice Iavernaro , Vincenzo Vespri

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

Explicit integrating factor Runge-Kutta methods are attractive and popular in developing high-order maximum bound principle preserving time-stepping schemes for Allen-Cahn type gradient flows. However, they always suffer from the…

Numerical Analysis · Mathematics 2024-10-10 Hong-lin Liao , Xuping Wang , Cao Wen

We present a novel and general methodology for building second-order finite volume implicit-explicit Runge-Kutta numerical schemes for solving two-dimensional financial parabolic PDEs with mixed derivatives. The methods achieve second-order…

Dynamic systems have a fundamental relevance in the description of physical phenomena. The search for more accurate and faster numerical integration methods for the resolution of such systems is, therefore, an important topic of research.…

Computational Physics · Physics 2025-10-10 J. Avellar , L. G. S. Duarte , L. A. C. P. da Mota , L. O. Pereira

This paper investigates, a new class of fractional order Runge-Kutta (FORK) methods for numerical approximation to the solution of fractional differential equations (FDEs). By using the Caputo generalizedTaylor formula and the total…

Numerical Analysis · Mathematics 2023-03-06 F. Ghoreishi , R. Ghaffari

Complex dynamical networks appear in a wide range of physical, biological, and engineering systems. The coupling of subsystems with varying time scales often results in multirate behavior. During the simulation of highly integrated…

Numerical Analysis · Mathematics 2015-04-27 Stefan Klus

We propose a new method to prove the partitioned Runge--Kutta methods with symplectic conditions for determinate and stochastic Hamiltonian systems are symplectic. We utilize Gr\"obner basis technology which is the one of symbolic…

Numerical Analysis · Mathematics 2025-09-16 Xiaojing Zhang

We introduce a new class of Runge-Kutta type methods suitable for time stepping to propagate hyperbolic solutions within tent-shaped spacetime regions. Unlike standard Runge-Kutta methods, the new methods yield expected convergence…

Numerical Analysis · Mathematics 2020-02-28 Jay Gopalakrishnan , Joachim Schöberl , Christoph Wintersteiger

This paper is devoted to examining the stability of Runge-Kutta methods for solving nonlinear Volterra delay-integro-differential-algebraic equations (DIDAEs) with constant delay. Hybrid numerical schemes combining Runge-Kutta methods and…

Numerical Analysis · Mathematics 2025-08-19 Gehao Wang , Yuexin Yu

For a large class of fully nonlinear parabolic equations, which include gradient flows for energy functionals that depend on the solution gradient, the semidiscretization in time by implicit Runge-Kutta methods such as the Radau IIA methods…

Numerical Analysis · Mathematics 2016-06-14 Peer C. Kunstmann , Buyang Li , Christian Lubich

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 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

We introduce a family of stochastic optimization methods based on the Runge-Kutta-Chebyshev (RKC) schemes. The RKC methods are explicit methods originally designed for solving stiff ordinary differential equations by ensuring that their…

Optimization and Control · Mathematics 2022-02-01 Tony Stillfjord , Måns Williamson