KPZ equation limit of sticky Brownian motion

IF 1.7 2区 数学 Q1 MATHEMATICS Journal of Functional Analysis Pub Date : 2024-07-30 DOI:10.1016/j.jfa.2024.110609
Sayan Das , Hindy Drillick , Shalin Parekh
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Abstract

We consider the motion of a particle under a continuum random environment whose distribution is given by the Howitt-Warren flow. In the moderate deviation regime, we establish that the quenched density of the motion of the particle (after appropriate centering and scaling) converges weakly to the (1+1) dimensional stochastic heat equation driven by multiplicative space-time white noise. Our result confirms physics predictions and computations in [66], [7] and is the first rigorous instance of such weak convergence in the moderate deviation regime. Our proof relies on a certain Girsanov transform and works for all Howitt-Warren flows with finite and nonzero characteristic measures. Our results capture universality in the sense that the limiting distribution depends on the flow only via the total mass of the characteristic measure. As a corollary of our results, we prove that the fluctuations of the maximum of an N-point sticky Brownian motion are given by the KPZ equation plus an independent Gumbel on timescales of order (logN)2.

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粘性布朗运动的 KPZ 方程极限
我们考虑了粒子在连续随机环境下的运动,该环境的分布由 Howitt-Warren 流给出。在适度偏差机制中,我们确定粒子运动的淬火密度(经过适当的定心和缩放)弱收敛于乘法时空白噪声驱动的维度随机热方程。我们的结果证实了物理学的预测和计算,也是在中等偏差机制下这种弱收敛的第一个严格实例。我们的证明依赖于某种吉尔萨诺夫变换,适用于所有具有有限和非零特征量的 Howitt-Warren 流。我们的结果抓住了普遍性,即极限分布仅通过特征量的总质量取决于流。作为我们结果的一个推论,我们证明了-点粘性布朗运动最大值的波动是由 KPZ 方程加上一个独立的 Gumbel 在时间尺度上给出的。
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来源期刊
CiteScore
3.20
自引率
5.90%
发文量
271
审稿时长
7.5 months
期刊介绍: The Journal of Functional Analysis presents original research papers in all scientific disciplines in which modern functional analysis plays a basic role. Articles by scientists in a variety of interdisciplinary areas are published. Research Areas Include: • Significant applications of functional analysis, including those to other areas of mathematics • New developments in functional analysis • Contributions to important problems in and challenges to functional analysis
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