2021
DOI: 10.48550/arxiv.2101.07846
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Parallel-in-time high-order multiderivative IMEX solvers

Abstract: In this work, we present a novel class of parallelizable high-order time integration schemes for the approximate solution of additive ODEs. The methods achieve high order through a combination of a suitable quadrature formula involving multiple derivatives of the ODE's right-hand side and a predictor-corrector ansatz. The latter approach is designed in such a way that parallelism in time is made possible. We present thorough analysis as well as numerical results that showcase scaling opportunities of methods f… Show more

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Cited by 1 publication
(9 citation statements)
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“…In this section, the semi-discrete in time formulation of the novel scheme is reviewed. For that purpose, we briefly recall the serial algorithm from [20] and [21] and slightly modify it. The algorithm describes a predictor-corrector approach with k max correction steps to approximate a two-derivative Hermite-Birkhoff Runge-Kutta method of order q and is therefore labeled as HBPC(q, k max ).…”
Section: Semi-discrete Formulationmentioning
confidence: 99%
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“…In this section, the semi-discrete in time formulation of the novel scheme is reviewed. For that purpose, we briefly recall the serial algorithm from [20] and [21] and slightly modify it. The algorithm describes a predictor-corrector approach with k max correction steps to approximate a two-derivative Hermite-Birkhoff Runge-Kutta method of order q and is therefore labeled as HBPC(q, k max ).…”
Section: Semi-discrete Formulationmentioning
confidence: 99%
“…The algorithm describes a predictor-corrector approach with k max correction steps to approximate a two-derivative Hermite-Birkhoff Runge-Kutta method of order q and is therefore labeled as HBPC(q, k max ). As we do not use an IMEX splitting in this paper as it is done in [20,21], the predictor is modified such that a fourth-order two-point Hermite-Birkhoff Runge-Kutta method is successively used to obtain the predicted solution. Please note that for convenience, we stick with the name HBPC(q, k max ) although we use a different predictor than in the original publication [21].…”
Section: Semi-discrete Formulationmentioning
confidence: 99%
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