随机方程的奇异极限

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY
D. Blomker, Jonas M. Tolle
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引用次数: 1

摘要

研究了随机演化方程在噪声强度消失和正则性不足的相互作用下的奇异极限,其中有噪声的极限下的方程由于缺乏正则性而不能定义。我们恢复了先前已知的小噪声随粗糙度增加而消失的结果,但我们的主要重点是研究固定噪声下方程中阶微分算子可能消失的奇异极限。尽管噪声在极限情况下逐渐消失,但由于重归一化效应,额外的确定性项出现了。我们将方程的分析与随机项的收敛分离开来,并给出了主要误差估计的一般框架。这首先将结果简化为残差上的边界,然后在第二步中将结果简化为随机卷积上的各种边界。此外,作为实例,我们将我们的结果应用于二维空间中具有消失的Bilaplacian的奇异正则化Allen-Cahn方程和具有时空白噪声的Cahn-Hilliard/Allen-Cahn同伦。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Singular limits for stochastic equations
We study singular limits of stochastic evolution equations in the interplay of disappearing strength of the noise and insufficient regularity, where the equation in the limit with noise would not be defined due to lack of regularity. We recover previously known results on vanishing small noise with increasing roughness, but our main focus is to study for fixed noise the singular limit where the leading order differential operator in the equation may vanish. Although the noise is disappearing in the limit, additional deterministic terms appear due to renormalization effects. We separate the analysis of the equation from the convergence of stochastic terms and give a general framework for the main error estimates. This first reduces the result to bounds on a residual and in a second step to various bounds on the stochastic convolution. Moreover, as examples we apply our result to the a singularly regularized Allen-Cahn equation with a vanishing Bilaplacian, and the Cahn-Hilliard/Allen-Cahn homotopy with space-time white noise in two spatial dimensions.
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来源期刊
Stochastics and Dynamics
Stochastics and Dynamics 数学-统计学与概率论
CiteScore
1.70
自引率
0.00%
发文量
49
审稿时长
>12 weeks
期刊介绍: This interdisciplinary journal is devoted to publishing high quality papers in modeling, analyzing, quantifying and predicting stochastic phenomena in science and engineering from a dynamical system''s point of view. Papers can be about theory, experiments, algorithms, numerical simulation and applications. Papers studying the dynamics of stochastic phenomena by means of random or stochastic ordinary, partial or functional differential equations or random mappings are particularly welcome, and so are studies of stochasticity in deterministic systems.
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