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High order spatial discretizations with monotonicity properties are often desirable for the solution of hyperbolic PDEs. These methods can advantageously be coupled with high order strong stability preserving time discretizations. The…

Numerical Analysis · Mathematics 2014-03-27 Sigal Gottlieb , Zachary J. Grant , Daniel Higgs

High order strong stability preserving (SSP) time discretizations are advantageous for use with spatial discretizations with nonlinear stability properties for the solution of hyperbolic PDEs. The search for high order strong stability…

Numerical Analysis · Mathematics 2016-03-24 Andrew J. Christieb , Sigal Gottlieb , Zachary J. Grant , David C. Seal

When evolving in time the solution of a hyperbolic partial differential equation, it is often desirable to use high order strong stability preserving (SSP) time discretizations. These time discretizations preserve the monotonicity…

Numerical Analysis · Mathematics 2017-08-02 Sidafa Conde , Sigal Gottlieb , Zachary J. Grant , John N. Shadid

We investigate the strong stability preserving (SSP) property of two-step Runge-Kutta (TSRK) methods. We prove that all SSP TSRK methods belong to a particularly simple subclass of TSRK methods, in which stages from the previous step are…

Numerical Analysis · Mathematics 2012-01-13 David I. Ketcheson , Sigal Gottlieb , Colin B. Macdonald

Strong stability preserving (SSP) integrators for initial value ODEs preserve temporal monotonicity solution properties in arbitrary norms. All existing SSP methods, including implicit methods, either require small step sizes or achieve…

Numerical Analysis · Mathematics 2012-03-27 David I. Ketcheson

We apply the concept of effective order to strong stability preserving (SSP) explicit Runge-Kutta methods. Relative to classical Runge-Kutta methods, methods with an effective order of accuracy are designed to satisfy a relaxed set of order…

Numerical Analysis · Mathematics 2014-01-09 Yiannis Hadjimichael , Colin B. Macdonald , David I. Ketcheson , James H. Verner

High order strong stability preserving (SSP) time discretizations ensure the nonlinear non-inner-product strong stability properties of spatial discretizations suited for the stable simulation of hyperbolic PDEs. Over the past decade…

Numerical Analysis · Mathematics 2024-12-20 Sigal Gottlieb , Zachary J. Grant

Explicit Runge-Kutta methods are classical and widespread techniques in the numerical solution of ordinary differential equations (ODEs). Considering partial differential equations, spatial semidiscretisations can be used to obtain systems…

Numerical Analysis · Mathematics 2020-04-08 Hendrik Ranocha

Strong stability preserving (SSP) methods are designed primarily for time integration of nonlinear hyperbolic PDEs, for which the permissible SSP step size varies from one step to the next. We develop the first SSP linear multistep methods…

Numerical Analysis · Mathematics 2022-04-05 Yiannis Hadjimichael , David Ketcheson , Lajos Lóczi , Adrián Németh

High order strong stability preserving (SSP) time discretizations are often needed to ensure the nonlinear (and sometimes non-inner-product) strong stability properties of spatial discretizations specially designed for the solution of…

Numerical Analysis · Mathematics 2018-10-22 Zachary Grant , Sigal Gottlieb , David C Seal

Strong Stability Preserving (SSP) time integration schemes maintain stability of the forward Euler method for any initial value problem. However, only a small subset of Runge-Kutta (RK) methods are SSP, and many efficient high-order time…

Numerical Analysis · Mathematics 2026-01-28 Mohammad R. Najafian , Brian C. Vermeire

Strong stability preserving (SSP) Runge-Kutta methods are often desired when evolving in time problems that have two components that have very different time scales. Where the SSP property is needed, it has been shown that implicit and…

Numerical Analysis · Mathematics 2018-08-15 Sigal Gottlieb , Zachary J. Grant , Leah Isherwood

Problems that feature significantly different time scales, where the stiff time-step restriction comes from a linear component, implicit-explicit (IMEX) methods alleviate this restriction if the concern is linear stability. However, where…

Numerical Analysis · Mathematics 2019-04-16 Leah Isherwood , Zachary J. Grant , Sigal Gottlieb

We construct a family of embedded pairs for optimal strong stability preserving explicit Runge-Kutta methods of order $2 \leq p \leq 4$ to be used to obtain numerical solution of spatially discretized hyperbolic PDEs. In this construction,…

Numerical Analysis · Mathematics 2022-05-17 Sidafa Conde , Imre Fekete , John N. Shadid

A novel optimization procedure for the generation of stability polynomials of stabilized explicit Runge-Kutta methods is devised. Intended for semidiscretizations of hyperbolic partial differential equations, the herein developed approach…

Numerical Analysis · Mathematics 2024-03-19 Daniel Doehring , Gregor J. Gassner , Manuel Torrilhon

The analysis of strong-stability-preserving (SSP) linear multistep methods is extended to semi-discretized problems for which different terms on the right-hand side satisfy different forward Euler (or circle) conditions. Optimal additive…

Numerical Analysis · Mathematics 2022-04-05 Yiannis Hadjimichael , David I. Ketcheson

Optimal Strong Stability Preserving (SSP) Runge--Kutta methods has been widely investegated in the last decade and many open conjectures have been formulated. The iterated implicit midpoint rule has been observed numerically optimal in…

Numerical Analysis · Mathematics 2014-10-01 Tihamér A. Kocsis , Adrián Németh

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

In this work we present a class of high order unconditionally strong stability preserving (SSP) implicit multi-derivative Runge--Kutta schemes, and SSP implicit-explicit (IMEX) multi-derivative Runge--Kutta schemes where the time-step…

Numerical Analysis · Mathematics 2021-08-10 Sigal Gottlieb , Zachary J. Grant , Jingwei Hu , Ruiwen Shu

Stabilized methods (also called Chebyshev methods) are explicit methods with extended stability domains along the negative real axis. These methods are intended for large mildly stiff problems, originating mainly from parabolic PDEs. In…

Numerical Analysis · Mathematics 2023-03-30 Andrew Moisa , Boris Faleichik
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