Wong–Zakai Approximations of Stochastic Allen–Cahn Equation

Wong–Zakai Approximations of Stochastic Allen–Cahn Equation

Year:    2019

Author:    Zhihui Liu, Zhonghua Qiao

International Journal of Numerical Analysis and Modeling, Vol. 16 (2019), Iss. 5 : pp. 681–694

Abstract

We establish an unconditional and optimal strong convergence rate of Wong–Zakai type approximations in Banach space norm for a parabolic stochastic partial differential equation with monotone drift, including the stochastic Allen–Cahn equation, driven by an additive Brownian sheet. The key ingredient in the analysis is the full use of additive nature of the noise and monotonicity of the drift to derive a priori estimation for the solution of this equation. Then we use the factorization method and stochastic calculus in martingale type 2 Banach spaces to deduce sharp error estimation between the exact and approximate Ornstein–Uhlenbeck processes, in Banach space norm. Finally, we combine this error estimation with the aforementioned a priori estimation to deduce the desired strong convergence rate of Wong–Zakai type approximations.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/2019-IJNAM-13248

International Journal of Numerical Analysis and Modeling, Vol. 16 (2019), Iss. 5 : pp. 681–694

Published online:    2019-01

AMS Subject Headings:    Global Science Press

Copyright:    COPYRIGHT: © Global Science Press

Pages:    14

Keywords:    Stochastic Allen–Cahn equation Wong–Zakai approximations strong convergence rate.

Author Details

Zhihui Liu

Zhonghua Qiao