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arXiv 2404.06458math.AP
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一类具有 Fujita 型临界指数的演化方程的临界非线性

Critical non-linearity for some evolution equations with Fujita-type critical exponent

Giovanni Girardi

AI总结:

本文研究一类常系数非线性演化方程的 Cauchy 问题,将幂非线性推广为带修正因子 $\mu$ 的形式,并给出区分小数据整体解与无整体弱解区域的积分条件。

摘要:

我们考虑如下一类非线性演化方程的 Cauchy 问题:\\[L(\partial_t,\partial_x) u=F(\partial_t^\ell u),\quad (t,x)\in[0,\infty)\times\mathbb{R}^n;\\]其中,$L(\partial_t,\partial_x)$ 是一个具有常系数的线性偏微分算子,关于时间变量 $t$ 的阶数为 $m\geq1$,而 $\ell$ 是满足 $0\leq\ell\leq m-1$ 的自然数。对于 $L$ 的若干不同选择,许多作者研究了当 $F(s)=|s|^p$ 为幂非线性时该问题整体(时间)解的存在性,寻找一个临界指数 $p_c>1$,使得在超临界情形 $p>p_c$ 下存在小数据整体解,而在亚临界情形 $1<p<p_c$ 下,在对数据作适当符号假设时不存在整体弱解。在本文中,我们考虑形式为 $F(s)=|s|^p\mu(|s|)$ 的更一般非线性项;对于一大类模型,我们给出了关于 $\mu$ 的一个积分条件,该条件能够更精确地区分存在小数据整体(时间)解的区域与问题不存在整体(时间)弱解的区域,从而改进了关于幂型非线性临界指数的已有结果。

英文摘要:

We consider the Cauchy problem for a class of non-linear evolution equations in the form \[L(\partial_t,\partial_x) u=F(\partial_t^\ell u), \quad (t,x)\in [0,\infty)\times \mathbb{R}^n;\] here, $L(\partial_t,\partial_x)$ is a linear partial differential operator with constant coefficients, of order $m\geq 1$ with respect to the time variable $t$, and $\ell$ is a natural number satisfying $0\leq \ell\leq m-1$. For several different choices of $L$, many authors have investigated the existence of global (in time) solutions to this problem when $F(s)=|s|^p$ is a power non-linearity, looking for a \textit{critical exponent} $p_c>1$ such that global small data solutions exist in the supercritical case $p>p_c$, whereas no global weak solutions exist, under suitable sign assumptions on the data, in the subcritical case $1<p<p_c$. In the present paper we consider a more general non-linear term in the form $F(s)=|s|^pμ(|s|)$; for a large class of models, we provide an integral condition on $μ$ which allows to distinguish more precisely the region of existence of a global (in time) small data solution from that in which the problem admits no global (in time) weak solutions, refining the existing results about the critical exponents for power type non-linearities.

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