Regularization effect of noise on fully discrete approximation for stochastic reaction-diffusion equation near sharp interface limit

To capture and simulate geometric surface evolutions, one effective approach is based on the phase field methods. Among them, it is important to design and analyze numerical approximations whose error bound depends on the inverse of the diffuse interface thickness (denoted by \(\frac 1\epsilon\)) po...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Author Cui, Jianbo
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.04.2024
Subjects
Approximation
Error analysis
Mathematical analysis
Partial differential equations
Polynomials
Reaction-diffusion equations
Regularization
Thickness
Online AccessGet full text

Cover

More Information
Summary:To capture and simulate geometric surface evolutions, one effective approach is based on the phase field methods. Among them, it is important to design and analyze numerical approximations whose error bound depends on the inverse of the diffuse interface thickness (denoted by \(\frac 1\epsilon\)) polynomially. However, it has been a long-standing problem whether such numerical error bound exists for stochastic phase field equations. In this paper, we utilize the regularization effect of noise to show that near sharp interface limit, there always exists the weak error bound of numerical approximations, which depends on \(\frac 1\epsilon\) at most polynomially. To illustrate our strategy, we propose a polynomial taming fully discrete scheme and present novel numerical error bounds under various metrics. Our method of proof could be also extended to a number of other fully numerical approximations for semilinear stochastic partial differential equations (SPDEs).
ISSN:2331-8422