Stability of step size control based on a posteriori error estimates

Abstract A posteriori error estimates based on residuals can be used for reliable error control of numerical methods. Here, we consider them in the context of ordinary differential equations and Runge-Kutta methods. In particular, we take the approach of Dedner & Giesselmann (2016) and investigate it when used to select the time step size. We focus on step size control stability when combined with explicit Runge-Kutta methods and demonstrate that a standard I controller is unstable while more advanced PI and PID controllers can be designed to be stable. We compare the stability properties of residual-based estimators and classical error estimators based on an embedded Runge-Kutta method both analytically and in numerical experiments.

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Publication Details

Journal
Computational Science and Engineering
Published
2024-09-23
DOI
https://doi.org/10.1007/s44207-024-00001-0
Citations
1
Primary Topic
Numerical methods for differential equations
Type
article
Field-Weighted Citation Impact
0.73

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article

Stability of step size control based on a posteriori error estimates

Jan Giesselmann, Hendrik Ranocha
1 citations
Computational Science and Engineering
Numerical methods for differential equations
0.73
article

Stability of step size control based on a posteriori error estimates

Jan Giesselmann, Hendrik Ranocha
article en
1 citations

Abstract

Abstract A posteriori error estimates based on residuals can be used for reliable error control of numerical methods. Here, we consider them in the context of ordinary differential equations and Runge-Kutta methods. In particular, we take the approach of Dedner & Giesselmann (2016) and investigate it when used to select the time step size. We focus on step size control stability when combined with explicit Runge-Kutta methods and demonstrate that a standard I controller is unstable while more advanced PI and PID controllers can be designed to be stable. We compare the stability properties of residual-based estimators and classical error estimators based on an embedded Runge-Kutta method both analytically and in numerical experiments.

Computational Science and EngineeringVol. 1(1)
Johannes Gutenberg University Mainz (DE), Technische Universität Darmstadt (DE)
Deutsche Forschungsgemeinschaft, Daimler und Benz Stiftung
Openalex Percentile: Top 31%
Numerical methods for differential equations
0.73
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