Analysis of a Mixed Finite Element Method for Stochastic Cahn-Hilliard Equation with Multiplicative Noise

Analysis of a Mixed Finite Element Method for Stochastic Cahn-Hilliard Equation with Multiplicative Noise

Year:    2024

Author:    Yukun Li, Corey Prachniak, Yi Zhang

Communications in Computational Physics, Vol. 35 (2024), Iss. 4 : pp. 1003–1028

Abstract

This paper proposes and analyzes a novel fully discrete finite element scheme with an interpolation operator for stochastic Cahn-Hilliard equations with functional-type noise. The nonlinear term satisfies a one-sided Lipschitz condition and the diffusion term is globally Lipschitz continuous. The novelties of this paper are threefold. Firstly, the $L^2$-stability ($L^∞$ in time) and $H^2$-stability ($L^2$ in time) are proved for the proposed scheme. The idea is to utilize the special structure of the matrix assembled by the nonlinear term. None of these stability results has been proved for the fully implicit scheme in existing literature due to the difficulty arising from the interaction of the nonlinearity and the multiplicative noise. Secondly, higher moment stability in $L^2$-norm of the discrete solution is established based on the previous stability results. Thirdly, the Hölder continuity in time for the strong solution is established under the minimum assumption of the strong solution. Based on these findings, the strong convergence in $H^{−1}$-norm of the discrete solution is discussed. Several numerical experiments including stability and convergence are also presented to validate our theoretical results.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/cicp.OA-2023-0172

Communications in Computational Physics, Vol. 35 (2024), Iss. 4 : pp. 1003–1028

Published online:    2024-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    26

Keywords:    Stochastic Cahn-Hilliard equations multiplicative noise higher moment stability strong convergence.

Author Details

Yukun Li

Corey Prachniak

Yi Zhang