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Author:

Bao, W. (Bao, W..) | Ma, Y. (Ma, Y..) | Wang, C. (Wang, C..)

Indexed by:

EI Scopus SCIE

Abstract:

We establish optimal error bounds on time-splitting methods for the nonlinear Schrödinger equation with low regularity potential and typical power-type nonlinearity f(ρ) = ρσ, where ρ:= |ψ|2 is the density with ψ the wave function and σ > 0 the exponent of the nonlinearity. For the first-order Lie-Trotter time-splitting method, optimal L2-norm error bound is proved for L∞-potential and σ > 0, and optimal H1-norm error bound is obtained for W1,4-potential and σ ≥ 1/2. For the second-order Strang time-splitting method, optimal L2-norm error bound is established for H2-potential and σ ≥ 1, and optimal H1-norm error bound is proved for H3-potential and σ ≥ 3/2 (or σ = 1). Compared to those error estimates of time-splitting methods in the literature, our optimal error bounds either improve the convergence rates under the same regularity assumptions or significantly relax the regularity requirements on potential and nonlinearity for optimal convergence orders. A key ingredient in our proof is to adopt a new technique called regularity compensation oscillation (RCO), where low frequency modes are analyzed by phase cancellation, and high frequency modes are estimated by regularity of the solution. Extensive numerical results are reported to confirm our error estimates and to demonstrate that they are sharp.  © 2024 World Scientific Publishing Company.

Keyword:

time-splitting method Nonlinear Schrödinger equation optimal error bound regularity compensation oscillation (RCO) low regularity potential low regularity nonlinearity

Author Community:

  • [ 1 ] [Bao W.]Department of Mathematics, National University of Singapore, Singapore, 119076, Singapore
  • [ 2 ] [Ma Y.]Department of Mathematics, School of Mathematics Statistics and Mechanics, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Wang C.]Department of Mathematics, National University of Singapore, Singapore, 119076, Singapore

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Source :

Mathematical Models and Methods in Applied Sciences

ISSN: 0218-2025

Year: 2024

Issue: 5

Volume: 34

Page: 803-844

3 . 5 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 7

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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