二维受梯度型噪声扰动的卡恩-希利亚德-纳维尔-斯托克斯方程的良好拟合

IF 1.6 2区 数学 Q2 MATHEMATICS, APPLIED Applied Mathematics and Optimization Pub Date : 2024-04-03 DOI:10.1007/s00245-024-10121-w
Ionuţ Munteanu
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引用次数: 0

摘要

在这项工作中,我们研究了带有梯度型噪声的随机卡恩-希利亚德-纳维尔-斯托克斯系统解的存在性和唯一性问题。我们证明,这类噪声与湍流建模问题有关。我们运用重定标论证将随机系统转化为随机确定性系统。我们将后者分为两部分:纳维-斯托克斯部分和卡恩-希利亚德部分。重标度算子具有良好的特性,这使得我们可以利用(\Δ -\)单调算子理论来证明重标度纳维-斯托克斯方程具有唯一的解。同时,通过定点论证证明了 Cahn-Hilliard 部分的好求解性。然后,再次使用定点论证来证明初始系统唯一解的全局时间存在性。所有结果都要求初始数据位于原点的某个小邻域内。
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Well-posedness for the Cahn-Hilliard-Navier-Stokes Equations Perturbed by Gradient-Type Noise, in Two Dimensions

In this work, we study the problem of existence and uniqueness of solutions of the stochastic Cahn-Hilliard-Navier-Stokes system with gradient-type noise. We show that such kind of noise is related to the problem of modelling turbulence. We apply a rescaling argument to transform the stochastic system into a random deterministic one. We split the latter into two parts: the Navier-Stokes part and the Cahn-Hilliard part, respectively. The rescale operators possess good properties which allow to show that the rescaled Navier-Stokes equations have a unique solution, by appealing to \(\delta -\)monotone operators theory. While, well-posedness of the Cahn-Hilliard part is proved via a fixed point argument. Then, again a fixed point argument is used to prove global in time existence of a unique solution to the initial system. All the results are under the requirement that the initial data is in a certain small neighbourhood of the origin.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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