scholarly journals Local well-posedness for the Zakharov system in dimension $ d = 2, 3 $

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Zijun Chen ◽  
Shengkun Wu

<p style='text-indent:20px;'>The Zakharov system in dimension <inline-formula><tex-math id="M2">\begin{document}$ d = 2,3 $\end{document}</tex-math></inline-formula> is shown to have a local unique solution for any initial values in the space <inline-formula><tex-math id="M3">\begin{document}$ H^{s} \times H^{l} \times H^{l-1} $\end{document}</tex-math></inline-formula>, where a new range of regularity <inline-formula><tex-math id="M4">\begin{document}$ (s, l) $\end{document}</tex-math></inline-formula> is given, especially at the line <inline-formula><tex-math id="M5">\begin{document}$ s-l = -1 $\end{document}</tex-math></inline-formula>. The result is obtained mainly by the normal form reduction and the Strichartz estimates.</p>

2017 ◽  
Vol 14 (01) ◽  
pp. 157-192 ◽  
Author(s):  
Yung-Fu Fang ◽  
Hsi-Wei Shih ◽  
Kuan-Hsiang Wang

We consider the quantum Zakharov system in one spatial dimension and establish a local well-posedness theory when the initial data of the electric field and the deviation of the ion density lie in a Sobolev space with suitable regularity. As the quantum parameter approaches zero, we formally recover a classical result by Ginibre, Tsutsumi, and Velo. We also improve their result concerning the Zakharov system and a result by Jiang, Lin, and Shao concerning the quantum Zakharov system.


2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Bixiang Wang

<p style='text-indent:20px;'>This paper deals with the asymptotic behavior of the non-autonomous random dynamical systems generated by the wave equations with supercritical nonlinearity driven by colored noise defined on <inline-formula><tex-math id="M1">\begin{document}$ \mathbb{R}^n $\end{document}</tex-math></inline-formula> with <inline-formula><tex-math id="M2">\begin{document}$ n\le 6 $\end{document}</tex-math></inline-formula>. Based on the uniform Strichartz estimates, we prove the well-posedness of the equation in the natural energy space and define a continuous cocycle associated with the solution operator. We also establish the existence and uniqueness of tempered random attractors of the equation by showing the uniform smallness of the tails of the solutions outside a bounded domain in order to overcome the non-compactness of Sobolev embeddings on unbounded domains.</p>


2020 ◽  
Vol 34 ◽  
pp. 03011
Author(s):  
Constantin Niţă ◽  
Laurenţiu Emanuel Temereancă

In this article we prove that the heat equation with a memory term on the one-dimensional torus has a unique solution and we study the smoothness properties of this solution. These properties are related with some smoothness assumptions imposed to the initial data of the problem and to the source term.


2017 ◽  
Vol 3 ◽  
pp. 55-68 ◽  
Author(s):  
Mourad Aouati

A procedure for classifying objects in the space of N×2 factors-attributes that are incorrectly classified as a result of constructing a linear discriminant function is proposed. The classification accuracy is defined as the proportion of correctly classified objects that are incorrectly classified at the first stage of constructing a linear discriminant function. It is shown that, for improperly classified objects, the transition from use as the factors-attributes of their initial values to the use of the centers of gravity (COGs) of local clusters provides the possibility of improving the classification accuracy by 14%. The procedure for constructing local clusters and the principle of forming a classifying rule are proposed, the latter being based on converting the equation of the dividing line to the normal form and determining the sign of the deviation magnitude of the COGs of local clusters from the dividing line


Author(s):  
Federico Cacciafesta ◽  
Anne-Sophie de Suzzoni

Abstract We prove local in time Strichartz estimates for the Dirac equation on spherically symmetric manifolds. As an application, we give a result of local well-posedness for some nonlinear models.


Sign in / Sign up

Export Citation Format

Share Document