Convergence of a moving window method for the Schr\"odinger equation with potential on $\mathbb{R}^d$

TL;DR

This paper introduces a moving window method for solving the Schrödinger equation in unbounded space, providing rigorous error analysis and demonstrating convergence with numerical experiments.

Contribution

The paper develops a novel moving window framework that reduces the Schrödinger equation in ^d to a bounded domain, with rigorous error estimates and convergence analysis.

Findings

Achieves first-order convergence in time and /2-order in space for ^ regularity.

Half-order convergence under CFL condition for less regular data.

Numerical examples confirm theoretical error estimates and method effectiveness.

Abstract

We propose a novel framework, called moving window method, for solving the linear Schr\"odinger equation with an external potential in $\mathbb{R}^d$. This method employs a smooth cut-off function to truncate the equation from Cauchy boundary conditions in the whole space to a bounded window of scaled torus, which is itself moving with the solution. This allows for the application of established schemes on this scaled torus to design algorithms for the whole-space problem. Rigorous analysis of the error in approximating the whole-space solution by numerical solutions on a bounded window is established. Additionally, analytical tools for periodic cases are used to rigorously estimate the error of these whole-space algorithms. By integrating the proposed framework with a classical first-order exponential integrator on the scaled torus, we demonstrate that the proposed scheme achieves first-order convergence in time and $\gamma/2$-order convergence in space for initial data in $H^\gamma(\mathbb{R}^d) \cap L^2(\mathbb{R}^d;|x|^{2\gamma} dx)$ with $\gamma \geq 2$. In the case where $\gamma = 1$, the numerical scheme is shown to have half-order convergence under an additional CFL condition. In practice, we can dynamically adjust the window when waves reach its boundary, allowing for continued computation beyond the initial window. Extensive numerical examples are presented to support the theoretical analysis and demonstrate the effectiveness of the proposed method.