Nonsmooth data error estimates for exponential Runge-Kutta methods and applications to split exponential integrators

We derive rigorous error bounds for exponential Runge-Kutta discretizations of parabolic equations with nonsmooth initial data. Our analysis is carried out in the framework of abstract semi-linear evolution equations, allowing for operators with non-dense domains. The results provide a foundation for establishing error estimates for nonsmooth data in prototypical problems such as the Allen-Cahn and Burgers equations. Furthermore, we apply these estimates to the analysis of split exponential integrators, yielding convergence results expressed explicitly in terms of the prescribed data.

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2026-03-24
DOI
https://doi.org/10.1051/m2an/2026009/pdf
Primary Topic
Numerical methods for differential equations
Type
article
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article

Nonsmooth data error estimates for exponential Runge-Kutta methods and applications to split exponential integrators

Gangfan Zhong, Qiumei Huang, Alexander Ostermann
Springer Link (Chiba Institute of Technology)
Numerical methods for differential equations
article

Nonsmooth data error estimates for exponential Runge-Kutta methods and applications to split exponential integrators

Gangfan Zhong, Qiumei Huang, Alexander Ostermann
article en

Abstract

We derive rigorous error bounds for exponential Runge-Kutta discretizations of parabolic equations with nonsmooth initial data. Our analysis is carried out in the framework of abstract semi-linear evolution equations, allowing for operators with non-dense domains. The results provide a foundation for establishing error estimates for nonsmooth data in prototypical problems such as the Allen-Cahn and Burgers equations. Furthermore, we apply these estimates to the analysis of split exponential integrators, yielding convergence results expressed explicitly in terms of the prescribed data.

Springer Link (Chiba Institute of Technology)
Reduced inequalities
Openalex Percentile: Top 74%
Numerical methods for differential equations
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