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作者:

Bao, Weizhu (Bao, Weizhu.) | Ma, Ying (Ma, Ying.) | Wang, Chushan (Wang, Chushan.)

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EI Scopus SCIE

摘要:

We establish optimal error bounds on time-splitting methods for the nonlinear Schrodinger equation with low regularity potential and typical power-type nonlinearity f(rho) = rho(sigma), where rho := |psi|(2) is the density with psi the wave function and sigma > 0 the exponent of the nonlinearity. For the first-order Lie-Trotter time-splitting method, optimal L-2-norm error bound is proved for L-infinity-potential and sigma > 0, and optimal H-1-norm error bound is obtained for W-1,W-4-potential and sigma >= 1/2. For the second-order Strang time-splitting method, optimal L-2-norm error bound is established for H-2-potential and sigma >= 1, and optimal H-1-norm error bound is proved for H-3-potential and sigma >= 3/2 (or sigma = 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.

关键词:

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

作者机构:

  • [ 1 ] [Bao, Weizhu]Natl Univ Singapore, Dept Math, Singapore 119076, Singapore
  • [ 2 ] [Wang, Chushan]Natl Univ Singapore, Dept Math, Singapore 119076, Singapore
  • [ 3 ] [Ma, Ying]Beijing Univ Technol, Sch Math Stat & Mech, Beijing 100124, Peoples R China

通讯作者信息:

  • [Wang, Chushan]Natl Univ Singapore, Dept Math, Singapore 119076, Singapore

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来源 :

MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES

ISSN: 0218-2025

年份: 2024

期: 05

卷: 34

页码: 803-844

3 . 5 0 0

JCR@2022

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