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SIAM Journal on Numerical Analysis· 2019Q1

Maximum Principle Preserving Exponential Time Differencing Schemes for the Nonlocal Allen--Cahn Equation

Qiang Du, Lili Ju, Xiao Li, Zhonghua Qiao

Short summary

New first and second order exponential time differencing schemes unconditionally preserve the discrete maximum principle for the nonlocal Allen--Cahn equation.

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Key points

  • Developed first and second order exponential time differencing schemes for the nonlocal Allen--Cahn equation.
  • Schemes unconditionally preserve the discrete maximum principle.
  • Derived optimal maximum-norm error estimates for the schemes.
  • Demonstrated asymptotic compatibility with the classic Allen--Cahn equation.
  • Proved discrete energy stability for the proposed numerical methods.

AI-generated from the title and abstract; the full text is not read.

Abstract

The nonlocal Allen--Cahn equation, a generalization of the classic Allen--Cahn equation by replacing the Laplacian with a parameterized nonlocal diffusion operator, satisfies the maximum principle as its local counterpart. In this paper, we develop and analyze first and second order exponential time differencing schemes for solving the nonlocal Allen--Cahn equation, which preserve the discrete maximum principle unconditionally. The fully discrete numerical schemes are obtained by applying the stabilized exponential time differencing approximations for time integration with quadrature-based finite difference discretization in space. We derive their respective optimal maximum-norm error estimates and further show that the proposed schemes are asymptotically compatible, i.e., the approximating solutions always converge to the classic Allen--Cahn solution when the horizon, the spatial mesh size, and the time step size go to zero. We also prove that the schemes are energy stable in the discrete sense. Various experiments are performed to verify these theoretical results and to investigate numerically the relation between the discontinuities and the nonlocal parameters.

The authors' abstract, as published at the source. SIAM Journal on Numerical Analysis, 2019 · DOI ↗

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Field: Numerical Analysis

Numerical AnalysisMathematics