含间接信号产生的抛物-抛物-椭圆趋化性模型的有限时间爆破

IF 1.7 2区 数学 Q2 MATHEMATICS, APPLIED
Xuan Mao, Yuxiang Li
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引用次数: 0

摘要

本文关注的是考虑间接信号产生的三组分趋化性模型,读作\(u_t = \nabla \cdot (\nabla u - u\nabla v)\), \(v_t = \Delta v - v + w\)和\(0 = \Delta w - w + u\),在\(\mathbb {R}^n\)和\(n\ge 5\)的球中构成,服从齐次诺伊曼边界条件。根据Fujie和Senba的开创性工作,该系统是其完全抛物版本的永井型变体,具有有限或无限时间内爆炸的四维临界质量现象[J]。微分方程,263 (2017),88-148;[j].中国科学院学报(自然科学版),2016,42 - 44。证明了对于任意规定质量\(m > 0\),存在具有\(\int _\Omega u_0 = m\)的径向对称正初始数据\((u_0,v_0)\in C^0(\overline{\Omega })\times C^2(\overline{\Omega })\),使得其解在有限时间内爆破。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finite-Time Blowup in a Parabolic-Parabolic-Elliptic Chemotaxis Model Involving Indirect Signal Production

This paper is concerned with a three-component chemotaxis model accounting for indirect signal production, reading as \(u_t = \nabla \cdot (\nabla u - u\nabla v)\), \(v_t = \Delta v - v + w\) and \(0 = \Delta w - w + u\), posed in a ball of \(\mathbb {R}^n\) with \(n\ge 5\), subject to homogeneous Neumann boundary conditions. The system is a Nagai-type variant of its fully parabolic version that has a four-dimensional critical mass phenomenon concerning blowup in finite or infinite time according to the seminal works of Fujie and Senba [J. Differential Equations, 263 (2017), 88–148; 266 (2019), 942–976]. We prove that for any prescribed mass \(m > 0\), there exist radially symmetric and positive initial data \((u_0,v_0)\in C^0(\overline{\Omega })\times C^2(\overline{\Omega })\) with \(\int _\Omega u_0 = m\) such that the corresponding solutions blow up in finite time.

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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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