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Special issue on infinite dimensional dynamical systems and applications

Xiaoying Han, Auburn University, Auburn, AL, USA xzh0003@auburn.edu

Wan-Tong Li, Lanzhou University, Chinawtli@lzu.edu.cn

Zhi-Cheng Wang, School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu 730000, Chinawangzhch@lzu.edu.cn

Asymptotic behaviour of a neural field lattice model with delaysSpecial Issues
Xiaoli Wang, Peter Kloeden and Meihua Yang
2020, 28(2): 1037-1048 doi: 10.3934/era.2020056 +[Abstract](20)+[HTML](11) +[PDF](331.1KB)
Abstract:

The asymptotic behaviour of an autonomous neural field lattice system with delays is investigated. It is based on the Amari model, but with the Heaviside function in the interaction term replaced by a sigmoidal function. First, the lattice system is reformulated as an infinite dimensional ordinary delay differential equation on weighted sequence state space \begin{document}$ \ell_\rho^2 $\end{document} under some appropriate assumptions. Then the global existence and uniqueness of its solution and its formulation as a semi-dynamical system on a suitable function space are established. Finally, the asymptotic behaviour of solution of the system is investigated, in particular, the existence of a global attractor is obtained.

The boundedness and upper semicontinuity of the pullback attractors for a 2D micropolar fluid flows with delaySpecial Issues
Wenlong Sun
2020, 28(3): 1343-1356 doi: 10.3934/era.2020071 +[Abstract](12)+[HTML](9) +[PDF](352.18KB)
Abstract:

In this paper, two properties of the pullback attractor for a 2D non-autonomous micropolar fluid flows with delay on unbounded domains are investigated. First, we establish the \begin{document}$ H^1 $\end{document}-boundedness of the pullback attractor. Further, with an additional regularity limit on the force and moment with respect to time t, we remark the \begin{document}$ H^2 $\end{document}-boundedness of the pullback attractor. Then, we verify the upper semicontinuity of the pullback attractor with respect to the domains.

Strong $ (L^2,L^\gamma\cap H_0^1) $-continuity in initial data of nonlinear reaction-diffusion equation in any space dimensionSpecial Issues
Hongyong Cui, Peter E. Kloeden and Wenqiang Zhao
2020, 28(3): 1357-1374 doi: 10.3934/era.2020072 +[Abstract](15)+[HTML](8) +[PDF](384.95KB)
Abstract:

In this paper we study the continuity in initial data of a classical reaction-diffusion equation with arbitrary \begin{document}$ p>2 $\end{document} order nonlinearity and in any space dimension \begin{document}$ N \geqslant 1 $\end{document}. It is proved that the weak solutions can be \begin{document}$ (L^2, L^\gamma\cap H_0^1) $\end{document}-continuous in initial data for arbitrarily large \begin{document}$ \gamma \geqslant 2 $\end{document} (independent of the physical parameters of the system), i.e., can converge in the norm of any \begin{document}$ L^\gamma\cap H_0^1 $\end{document} as the corresponding initial values converge in \begin{document}$ L^2 $\end{document}. In fact, the system is shown to be \begin{document}$ (L^2, L^\gamma\cap H_0^1) $\end{document}-smoothing in a H\begin{document}$ \ddot{\rm o} $\end{document}lder way. Applying this to the global attractor we find that, with external forcing only in \begin{document}$ L^2 $\end{document}, the attractor \begin{document}$ \mathscr{A} $\end{document} attracts bounded subsets of \begin{document}$ L^2 $\end{document} in the norm of any \begin{document}$ L^\gamma\cap H_0^1 $\end{document}, and that every translation set \begin{document}$ \mathscr{A}-z_0 $\end{document} of \begin{document}$ \mathscr{A} $\end{document} for any \begin{document}$ z_0\in \mathscr{A} $\end{document} is a finite dimensional compact subset of \begin{document}$ L^\gamma\cap H_0^1 $\end{document}. The main technique we employ is a combination of a Moser iteration and a decomposition of the nonlinearity, by which the interpolation inequalities are avoided and the new continuity result is obtained without any restrictions on the order \begin{document}$ p>2 $\end{document} of the nonlinearity and the space dimension \begin{document}$ N \geqslant 1 $\end{document}.

Odd random attractors for stochastic non-autonomous Kuramoto-Sivashinsky equations without dissipationSpecial Issues
Yangrong Li, Shuang Yang and Qiangheng Zhang
2020, 28(4): 1529-1544 doi: 10.3934/era.2020080 +[Abstract](15)+[HTML](14) +[PDF](356.71KB)
Abstract:

We study the random dynamics for the stochastic non-autonomous Kuramoto-Sivashinsky equation in the possibly non-dissipative case. We first prove the existence of a pullback attractor in the Lebesgue space of odd functions, then show that the fiber of the odd pullback attractor semi-converges to a nonempty compact set as the time-parameter goes to minus infinity and finally prove the measurability of the attractor. In a word, we obtain a longtime stable random attractor formed from odd functions. A key tool is the existence of a bridge function between Lebesgue and Sobolev spaces of odd functions.

Regular random attractors for non-autonomous stochastic reaction-diffusion equations on thin domainsSpecial Issues
Dingshi Li and Xuemin Wang
2021, 29(2): 1969-1990 doi: 10.3934/era.2020100 +[Abstract](576)+[HTML](306) +[PDF](462.41KB)
Abstract:

This paper deals with the limiting dynamical behavior of non-autonomous stochastic reaction-diffusion equations on thin domains. Firstly, we prove the existence and uniqueness of the regular random attractor. Then we prove the upper semicontinuity of the regular random attractors for the equations on a family of \begin{document}$ (n+1) $\end{document}-dimensional thin domains collapses onto an \begin{document}$ n $\end{document}-dimensional domain.

Pullback attractors for stochastic recurrent neural networks with discrete and distributed delaysSpecial Issues
Meiyu Sui, Yejuan Wang and Peter E. Kloeden
2021, 29(2): 2187-2221 doi: 10.3934/era.2020112 +[Abstract](598)+[HTML](273) +[PDF](511.36KB)
Abstract:

In this paper, we investigate a class of stochastic recurrent neural networks with discrete and distributed delays for both biological and mathematical interests. We do not assume any Lipschitz condition on the nonlinear term, just a continuity assumption together with growth conditions so that the uniqueness of the Cauchy problem fails to be true. Moreover, the existence of pullback attractors with or without periodicity is presented for the multi-valued noncompact random dynamical system. In particular, a new method for checking the asymptotical compactness of solutions to the class of nonautonomous stochastic lattice systems with infinite delay is used.

A multiscale stochastic criminal behavior model under a hybrid schemeSpecial Issues
Chuntian Wang and Yuan Zhang
2021, 29(4): 2741-2753 doi: 10.3934/era.2021011 +[Abstract](278)+[HTML](175) +[PDF](1163.89KB)
Abstract:

Crime in urban environment is a major social problem nowadays. As such, many efforts have been made to develop mathematical models for this type of crime. The pioneering work [M. B. Short, M. R. D'Orsogna, V. B. Pasour, G. E. Tita, P. J. Brantingham, A. L. Bertozzi and L. B. Chayes, Math. Models Methods Appl. Sci., 18, (2008), pp. 1249-1267] establishes an agent-based human-environment interaction model of criminal behavior for residential burglary, where aggregate pattern formation of "hotspots" is quantitatively studied for the first time. Potential offenders are assumed to interact with environment according to well-known criminology and sociology notions. However long-term simulations for the coupled dynamics are computationally costly due to all components evolving on slow time scales. In this paper, we introduce a new-generation criminal behavior model with separated spatio-temporal scales for the agent actions and the environment parameter reactions. The computational cost is reduced significantly, while the essential stochastic features of the pioneering model are preserved. Moreover, the separation of scales brings the model into the theoretical framework of piecewise deterministic Markov processes (PDMP). A martingale approach is applicable which will be useful to analyze both stochastic and statistical features of the model in subsequent studies.

2020 Impact Factor: 1.833
5 Year Impact Factor: 1.833

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