4.6 Article

An easy to implement linearized numerical scheme for fractional reaction-diffusion equations with a prehistorical nonlinear source function

期刊

MATHEMATICS AND COMPUTERS IN SIMULATION
卷 200, 期 -, 页码 218-239

出版社

ELSEVIER
DOI: 10.1016/j.matcom.2022.04.014

关键词

Fractional reaction-diffusion; Prehistory; L1 difference scheme; Galerkin-Legendre spectral method; Fractional Halanay inequalities; Discrete fractional Gr?nwall inequalities

资金

  1. Arab Republic of Egypt
  2. Russian Federation
  3. National Research Centre of Egypt
  4. RSF grant, Russia [22-21-00075]

向作者/读者索取更多资源

In this paper, a linearized finite difference/Galerkin-Legendre spectral scheme is constructed and analyzed for the nonlinear Riesz-space and Caputo-time fractional reaction-diffusion equation with prehistory. The proposed method offers the advantage of a linear implementation of the iterative approach and is validated through numerical results.
In this paper, we construct and analyze a linearized finite difference/Galerkin-Legendre spectral scheme for the nonlinear Riesz-space and Caputo-time fractional reaction-diffusion equation with prehistory. The problem is first approximated by the L1 difference method in the temporal direction, and then the Galerkin-Legendre spectral method is applied for the spatial discretization. The key advantage of the proposed method is that the implementation of the iterative approach is linear. The stability and the convergence of the semi-discrete approximation are proved by invoking the discrete fractional Halanay inequality. The stability and convergence of the fully discrete scheme are also investigated utilizing discrete fractional Gronwall inequalities, which show that the proposed method is stable and convergent. Furthermore, to verify the efficiency of our method, we provide numerical results that show a satisfactory agreement with the theoretical analysis.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

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