关注自适应动态网络的最新进展。

IF 3.2 2区 数学 Q1 MATHEMATICS, APPLIED
Chaos Pub Date : 2025-10-01 DOI:10.1063/5.0300039
Serhiy Yanchuk, Erik Andreas Martens, Christian Kuehn, Jürgen Kurths
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引用次数: 0

摘要

自适应动态网络(ADNs)描述了网络节点状态和网络结构本身随时间共同演化的系统。这两个耦合的动态过程的相互作用是广泛的自然和技术现象的基础,如神经可塑性、学习和意见形成。ADNs固有的协同进化特性对数学理论和建模提出了重大挑战,近年来引起了人们的强烈兴趣并取得了快速进展。本刊重点介绍了25篇研究文章,重点介绍了该领域的最新发展,包括新的分析和计算技术,ADNs中新的动态现象的发现,以及ADNs在神经科学,地球科学,生物学,社会科学,机器学习和控制中的各种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Focus issue on recent advances in adaptive dynamical networks.

Adaptive dynamical networks (ADNs) describe systems in which the states of the network nodes and the network structure itself co-evolve over time. This interplay of two coupled dynamical processes underlies a wide range of natural and technological phenomena, such as neural plasticity, learning, and opinion formation. The inherently co-evolutionary nature of ADNs poses significant challenges to mathematical theory and modeling, driving strong interest and rapid advances in recent years. This Focus Issue presents 25 research articles highlighting recent developments in the field, including new analytical and computational techniques, the discovery of novel dynamical phenomena in ADNs, and diverse applications of ADNs in neuroscience, Earth science, biology, social sciences, machine learning and control.

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来源期刊
Chaos
Chaos 物理-物理:数学物理
CiteScore
5.20
自引率
13.80%
发文量
448
审稿时长
2.3 months
期刊介绍: Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.
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