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Stochastic parareal (SParareal) is a probabilistic variant of the popular parallel-in-time algorithm known as parareal. Similarly to parareal, it combines fine- and coarse-grained solutions to an ordinary differential equation (ODE) using a…

Numerical Analysis · Mathematics 2023-03-13 Kamran Pentland , Massimiliano Tamborrino , T. J. Sullivan

We consider the usage of parallel-in-time algorithms of the Parareal and multigrid-reduction-in-time (MGRIT) methodologies for the parallel-in-time solution of the eddy current problem. Via application of these methods to a two-dimensional…

Numerical Analysis · Mathematics 2018-11-01 Stephanie Friedhoff , Jens Hahne , Iryna Kulchytska-Ruchka , Sebastian Schöps

A class of abstract nonlinear time-periodic evolution problems is considered which arise in electrical engineering and other scientific disciplines. An efficient solver is proposed for the systems arising after discretization in time based…

Numerical Analysis · Mathematics 2025-03-03 Herbert Egger , Andreas Schafelner

A space-time fully adaptive multiresolution method for evolutionary non-linear partial differential equations is presented introducing an improved local time-stepping method. The space discretisation is based on classical finite volumes,…

Numerical Analysis · Mathematics 2019-05-22 Müller Moreira Lopes , Margarete Oliveira Domingues , Kai Schneider , Odim Mendes

A three-level explicit time-split MacCormack scheme is proposed for solving the two-dimensional nonlinear reaction-diffusion equations. The computational cost is reduced thank to the splitting and the explicit MacCormack scheme. Under the…

Numerical Analysis · Mathematics 2020-12-02 Eric Ngondiep

Many important initial value problems have the property that energy is non-increasing in time. Energy stable methods, also referred to as strongly stable methods, guarantee the same property discretely. We investigate requirements for…

Numerical Analysis · Mathematics 2020-11-26 Hendrik Ranocha , David I. Ketcheson

A time discretization method is called strongly stable, if the norm of its numerical solution is nonincreasing. It is known that, even for linear semi-negative problems, many explicit Runge--Kutta (RK) methods fail to preserve this…

Numerical Analysis · Mathematics 2019-12-30 Zheng Sun , Chi-Wang Shu

The work deals with two major topics concerning the numerical analysis of Runge-Kutta-like (RK-like) methods, namely their stability and order of convergence. RK-like methods differ from additive RK methods in that their coefficients are…

Numerical Analysis · Mathematics 2025-06-26 Thomas Izgin

The parareal algorithm is a powerful parallel-in-time integration method that accelerates the numerical solution of evolution equations by iteratively combining a fine propagator and a coarse propagator. Although the convergence of the…

Numerical Analysis · Mathematics 2026-05-28 Georgios Akrivis , Qingle Lin , Zhi Zhou

We introduce a class of exponential Runge-Kutta integration methods for kinetic equations. The methods are based on a decomposition of the collision operator into an equilibrium and a non equilibrium part and are exact for relaxation…

Numerical Analysis · Mathematics 2010-10-08 Giacomo Dimarco , Lorenzo Pareschi

Implicit-explicit Runge-Kutta (IMEX-RK) time discretization methods are very popular when solving stiff kinetic equations. In [21], an asymptotic analysis shows that a specific class of high-order IMEX-RK schemes can accurately capture the…

Numerical Analysis · Mathematics 2026-01-12 Sebastiano Boscarino , Seung Yeon Cho

In this work, we develop a class of up to third-order energy-stable schemes for the Cahn--Hilliard equation. Building on Lawson's integrating factor Runge--Kutta method, which is widely used for stiff semilinear equations, we discuss its…

Numerical Analysis · Mathematics 2024-11-26 Haifeng Wang , Jingwei Sun , Hong Zhang , Xu Qian , Songhe Song

In this article, we introduce parallel-in-time methods for state and parameter estimation in general nonlinear non-Gaussian state-space models using the statistical linear regression and the iterated statistical posterior linearization…

Computation · Statistics 2023-04-06 Fatemeh Yaghoobi , Adrien Corenflos , Sakira Hassan , Simo Särkkä

In numerical time-integration with implicit-explicit (IMEX) methods, a within-step adaptable decomposition called residual balanced decomposition is introduced. With this decomposition, the requirement of a small enough residual in the…

Numerical Analysis · Mathematics 2019-02-11 Savio B. Rodrigues

Modified Patankar-Runge-Kutta (MPRK) methods preserve the positivity as well as conservativity of a production-destruction system (PDS) of ordinary differential equations for all time step sizes. As a result, higher order MPRK schemes do…

Numerical Analysis · Mathematics 2022-10-31 Thomas Izgin , Stefan Kopecz , Andreas Meister

We present a full implementation of the parareal algorithm---an integration technique to solve differential equations in parallel---in the Julia programming language for a fully general, first-order, initial-value problem. We provide a…

Mathematical Software · Computer Science 2018-12-17 Tyler M. Masthay , Saverio Perugini

Parallel-across-the method time integration can provide small scale parallelism when solving initial value problems. Spectral deferred corrections (SDC) with a diagonal sweeper, which is closely related to iterated Runge-Kutta methods…

Numerical Analysis · Mathematics 2025-02-12 Gayatri Čaklović , Thibaut Lunet , Sebastian Götschel , Daniel Ruprecht

In this paper we combine the Parareal parallel-in-time method together with spatial parallelization and investigate this space-time parallel scheme by means of solving the three-dimensional incompressible Navier-Stokes equations.…

Computational Engineering, Finance, and Science · Computer Science 2017-05-18 Roberto Croce , Daniel Ruprecht , Rolf Krause

The class of stochastic Runge-Kutta methods for stochastic differential equations due to R\"o{\ss}ler is considered. Coefficient families of diagonally drift-implicit stochastic Runge-Kutta (DDISRK) methods of weak order one and two are…

Numerical Analysis · Mathematics 2016-05-10 Kristian Debrabant , Andreas Rößler

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