具有粗糙数据和随机数据的Ostrovsky方程的收敛性问题

IF 1.2 2区 数学 Q1 MATHEMATICS
Wei Yan, Qiaoqiao Zhang, Jinqiao Duan, Meihua Yang
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引用次数: 1

摘要

本文研究了具有粗糙数据和随机数据的自由Ostrovsky方程的点向收敛问题。首先,我们用粗糙数据$s\geq \frac{1}{4}$证明了$H^{s}(\mathbb{R})$中自由Ostrovsky方程的几乎处处点向收敛性。其次,我们给出了反例,表明与自由Ostrovsky方程相关的极大函数估计在$s<\frac{1}{4}$情况下可能失败。最后,对于每一个$x\in \mathbb{R}$,我们用随机数据证明了$L^{2}(\mathbb{R})$中自由Ostrovsky方程几乎肯定是点向收敛的。本文的主要工具是密度定理、高低频思想、维纳分解和引理2.1-2.6,以及一些随机序列的概率估计,这些引理仅为3.2-3.4。主要困难在于零是自由Ostrovsky方程相函数的奇点。我们用高频低频的思想来克服困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convergence problem of Ostrovsky equation with rough data and random data
In this paper, we consider the pointwise convergence problem of free Ostrovsky equation with rough data and random data. Firstly, we show the almost everywhere pointwise convergence of free Ostrovsky equation in $H^{s}(\mathbb{R})$ with $s\geq \frac{1}{4}$ with rough data. Secondly, we present counterexamples showing that the maximal function estimate related to the free Ostrovsky equation can fail if $s<\frac{1}{4}$. Finally, for every $x\in \mathbb{R}$, we show the almost surely pointwise convergence of free Ostrovsky equation in $L^{2}(\mathbb{R})$ with random data. The main tools are the density theorem, high-low frequency idea, Wiener decomposition and Lemmas 2.1-2.6 as well as the probabilistic estimates of some random series which are just Lemmas 3.2-3.4 in this paper. The main difficulty is that zero is the singular point of the phase functions of free Ostrovsky equation. We use high-low frequency idea to conquer the difficulties.
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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
52
审稿时长
4.5 months
期刊介绍: Information not localized
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