AVERAGING PRINCIPLE FOR STOCHASTIC REAL GINZBURG-LANDAU EQUATION DRIVEN BY α-STABLE PROCESS

被引：10

作者：

Sun, Xiaobin ^{[1
]}

Zhai, Jianliang ^{[2
]}

机构：

[1] Jiangsu Normal Univ, Sch Math & Stat, Xuzhou 221116, Jiangsu, Peoples R China

[2] Univ Sci & Technol China, CAS Sch Math Sci, Key Lab Wu Wen Tsun Math, Hefei 230026, Anhui, Peoples R China

来源：

COMMUNICATIONS ON PURE AND APPLIED ANALYSIS | 2020年 / 19卷 / 03期

关键词：

Stochastic real Ginzburg-Landau equation; averaging principle; ergodicity; invariant measure; strong convergence; cylindrical alpha-stable; STRONG-CONVERGENCE RATE; SYSTEMS; ERGODICITY;

D O I：

10.3934/cpaa.2020063

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we study a system of stochastic partial differential equations with slow and fast time-scales, where the slow component is a stochastic real Ginzburg-Landau equation and the fast component is a stochastic reaction-diffusion equation, the system is driven by cylindrical alpha-stable process with alpha is an element of (1, 2). Using the classical Khasminskii approach based on time discretization and the techniques of stopping times, we show that the slow component strong converges to the solution of the corresponding averaged equation under some suitable conditions.

引用

页码：1291 / 1319

页数：29

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