Embedded exponential-type low-regularity integrators for KdV equation under rough data

• Published in: BIT Vol. 62; no. 3; pp. 1049 - 1090
• Main Authors: Wu, Yifei, Zhao, Xiaofei
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2022; Springer Nature B.V
• Subjects: Computational Mathematics and Numerical Analysis; Convergence; Error analysis; Integrators; Mathematics; Mathematics and Statistics; Numeric Computing; Regularity; 65L05; 65M15; Error estimates; Low-regularity integrator; 65L70; Rough data; Second order accuracy; First order accuracy; KdV equation; 65L20; 65M12
• ISSN: 0006-3835; 1572-9125
• DOI: 10.1007/s10543-021-00895-8

In this paper, we introduce a novel class of embedded exponential-type low-regularity integrators (ELRIs) for solving the KdV equation and establish their optimal convergence results under rough initial data. The schemes are explicit and efficient to implement. By rigorous error analysis, we first show that the ELRI scheme provides the first order accuracy in H γ for initial data in H γ + 1 for γ > 1 2 . Moreover, by adding two more correction terms to the first order scheme, we show a second order ELRI that provides the second order accuracy in H γ for initial data in H γ + 3 for γ ≥ 0 . The proposed ELRIs further reduce the regularity requirement of existing methods so far for optimal convergence. The theoretical results are confirmed by numerical experiments, and comparisons with existing methods illustrate the efficiency of the new methods.