Microscopic derivation of non-local models with anomalous diffusions from stochastic particle systems

IF 1.3 2区 数学 Q1 MATHEMATICS
Marielle Simon , Christian Olivera
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引用次数: 0

Abstract

This paper considers a large class of nonlinear integro-differential scalar equations which involve an anomalous diffusion (e.g. driven by a fractional Laplacian) and a non-local singular convolution kernel. Each of those singular equations is obtained as the macroscopic limit of an interacting particle system modeled as N coupled stochastic differential equations driven by Lévy processes. In particular we derive quantitative estimates between the microscopic empirical measure of the particle system and the solution to the limit equation in some non-homogeneous Sobolev space. Our result only requires very weak regularity on the interaction kernel, therefore it includes numerous applications, e.g.: the 2d turbulence model (including the quasi-geostrophic equation) in sub-critical regime, the 2d generalized Navier–Stokes equation, the fractional Keller–Segel equation in any dimension, and the fractal Burgers equation.
随机粒子系统异常扩散非局部模型的微观推导
考虑了一类非线性积分-微分标量方程,该方程包含一个反常扩散(例如由分数阶拉普拉斯算子驱动)和一个非局部奇异卷积核。每一个奇异方程都是相互作用粒子系统的宏观极限,该系统被建模为由lsamvy过程驱动的N个耦合随机微分方程。特别地,我们导出了非齐次Sobolev空间中粒子系统的微观经验测度与极限方程解之间的定量估计。我们的结果只需要在相互作用核上有非常弱的正则性,因此它有许多应用,例如:亚临界状态下的二维湍流模型(包括准地转方程),二维广义Navier-Stokes方程,任何维度的分数阶Keller-Segel方程,分形Burgers方程。
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来源期刊
CiteScore
3.30
自引率
0.00%
发文量
265
审稿时长
60 days
期刊介绍: Nonlinear Analysis focuses on papers that address significant problems in Nonlinear Analysis that have a sustainable and important impact on the development of new directions in the theory as well as potential applications. Review articles on important topics in Nonlinear Analysis are welcome as well. In particular, only papers within the areas of specialization of the Editorial Board Members will be considered. Authors are encouraged to check the areas of expertise of the Editorial Board in order to decide whether or not their papers are appropriate for this journal. The journal aims to apply very high standards in accepting papers for publication.
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