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arXiv 2304.05652nlin.AOmath.DS
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自适应动力学网络

Adaptive Dynamical Networks

Rico Berner, Thilo Gross, Christian Kuehn, Jürgen Kurths, Serhiy Yanchuk

AI总结:

本文综述了自适应动力学网络这一结构与功能相互影响的重要系统类别,系统介绍了其在不同领域的应用、动力学特征、涌现现象及现有数学分析方法。

摘要:

理解网络的功能与其结构组织之间的关系是一个根本性挑战。自适应动力学网络代表了一类广泛的系统,它们能够根据自身的动力学状态随时间改变其连接性。这类系统最重要的特征是它们的功能依赖于其结构,反之亦然。虽然静态网络的性质在过去已被广泛研究,但对自适应网络的研究则更具挑战性。此外,自适应动力学网络对于各种应用领域具有极其重要的意义,特别是对于神经元突触可塑性模型、化学、流行病、生物、交通和社会系统中的自适应网络等等。在这篇综述中,我们提供了自适应动力学网络的详细描述,展示了它们在各个研究领域中的应用,强调了它们的动力学特征并描述了所产生的动力学现象,同时概述了为理解自适应动力学网络而发展起来的可用数学方法。

英文摘要:

It is a fundamental challenge to understand how the function of a network is related to its structural organization. Adaptive dynamical networks represent a broad class of systems that can change their connectivity over time depending on their dynamical state. The most important feature of such systems is that their function depends on their structure and vice versa. While the properties of static networks have been extensively investigated in the past, the study of adaptive networks is much more challenging. Moreover, adaptive dynamical networks are of tremendous importance for various application fields, in particular, for the models for neuronal synaptic plasticity, adaptive networks in chemical, epidemic, biological, transport, and social systems, to name a few. In this review, we provide a detailed description of adaptive dynamical networks, show their applications in various areas of research, highlight their dynamical features and describe the arising dynamical phenomena, and give an overview of the available mathematical methods developed for understanding adaptive dynamical networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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