English

Long-time error bounds of low-regularity integrators for nonlinear Schr\"odinger equations

Numerical Analysis 2023-02-02 v1 Numerical Analysis

Abstract

We introduce a new non-resonant low-regularity integrator for the cubic nonlinear Schr\"odinger equation (NLSE) allowing for long-time error estimates which are optimal in the sense of the underlying PDE. The main idea thereby lies in treating the zeroth mode exactly within the discretization. For long-time error estimates, we rigorously establish the long-time error bounds of different low-regularity integrators for the nonlinear Schr\"odinger equation (NLSE) with small initial data characterized by a dimensionless parameter ε(0,1]\varepsilon \in (0, 1]. We begin with the low-regularity integrator for the quadratic NLSE in which the integral is computed exactly and the improved uniform first-order convergence in HrH^r is proven at O(ετ)O(\varepsilon \tau) for solutions in HrH^r with r>1/2r > 1/2 up to the time Tε=T/εT_{\varepsilon } = T/\varepsilon with fixed T>0T > 0. Then, the improved uniform long-time error bound is extended to a symmetric second-order low-regularity integrator in the long-time regime. For the cubic NLSE, we design new non-resonant first-order and symmetric second-order low-regularity integrators which treat the zeroth mode exactly and rigorously carry out the error analysis up to the time Tε=T/ε2T_{\varepsilon } = T/\varepsilon ^2. With the help of the regularity compensation oscillation (RCO) technique, the improved uniform error bounds are established for the new non-resonant low-regularity schemes, which further reduce the long-time error by a factor of ε2\varepsilon^2 compared with classical low-regularity integrators for the cubic NLSE. Numerical examples are presented to validate the error estimates and compare with the classical time-splitting methods in the long-time simulations.

Keywords

Cite

@article{arxiv.2302.00383,
  title  = {Long-time error bounds of low-regularity integrators for nonlinear Schr\"odinger equations},
  author = {Yue Feng and Georg Maierhofer and Katharina Schratz},
  journal= {arXiv preprint arXiv:2302.00383},
  year   = {2023}
}
R2 v1 2026-06-28T08:28:59.608Z