Long-term dynamics of fractional stochastic delay reaction–diffusion equations on unbounded domains

IF 1.4 3区 数学 Q2 MATHEMATICS, APPLIED
Zhang Chen, Bixiang Wang
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引用次数: 0

Abstract

In this paper, we investigate the long-term dynamics of fractional stochastic delay reaction-diffusion equations on unbounded domains with a polynomial drift term of arbitrary order driven by nonlinear noise. We first define a mean random dynamical system in a Hilbert space for the solutions of the equation and prove the existence and uniqueness of weak pullback mean random attractors. We then establish the existence and regularity of invariant measures of the system under further conditions on the nonlinear delay and diffusion terms. We also prove the tightness of the set of all invariant measures of the equation when the time delay varies in a bounded interval. We finally show that every limit of a sequence of invariant measures of the delay equation must be an invariant measure of the limiting system as delay approaches zero. The uniform tail-estimates and the Ascoli–Arzelà theorem are used to derive the tightness of distribution laws of solutions in order to overcome the non-compactness of Sobolev embeddings on unbounded domains.

无界域上分数随机延迟反应-扩散方程的长期动力学
本文研究了无界域上的分数随机延迟反应扩散方程的长期动力学,该方程具有由非线性噪声驱动的任意阶多项式漂移项。我们首先为方程的解定义了一个希尔伯特空间中的均值随机动力学系统,并证明了弱回拉均值随机吸引子的存在性和唯一性。然后,在非线性延迟和扩散项的进一步条件下,我们建立了该系统不变量的存在性和正则性。我们还证明了当时间延迟在有界区间内变化时,方程所有不变量集合的紧密性。最后,我们证明了当延迟趋近于零时,延迟方程不变量序列的每个极限都必须是极限系统的不变量。均匀尾估计和阿斯科利-阿尔泽拉定理被用来推导解的分布规律的紧密性,以克服索波列夫嵌入在无界域上的非紧密性。
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来源期刊
CiteScore
2.70
自引率
13.30%
发文量
54
期刊介绍: Stochastics and Partial Differential Equations: Analysis and Computations publishes the highest quality articles presenting significantly new and important developments in the SPDE theory and applications. SPDE is an active interdisciplinary area at the crossroads of stochastic anaylsis, partial differential equations and scientific computing. Statistical physics, fluid dynamics, financial modeling, nonlinear filtering, super-processes, continuum physics and, recently, uncertainty quantification are important contributors to and major users of the theory and practice of SPDEs. The journal is promoting synergetic activities between the SPDE theory, applications, and related large scale computations. The journal also welcomes high quality articles in fields strongly connected to SPDE such as stochastic differential equations in infinite-dimensional state spaces or probabilistic approaches to solving deterministic PDEs.
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