Advances in Adaptive Dynamical Networks

Adaptive (co-evolutionary) dynamical networks play an important role in modeling many challenging real-world systems, ranging from neural networks with plasticity to social or ecological networks. Such systems are characterized by a network structure, a dynamical function of its elements (nodes) and, most importantly, an interdependence between structure and function. In other words, the structure of such networks co-evolves with their dynamical state. Awareness of the importance of studying adaptive dynamical networks has recently grown rapidly, as it has become increasingly clear that the potential of dynamical networks with static structure cannot capture such challenging phenomena as neural plasticity, machine learning, adaptive control problems on networks, transport networks, and others. The recent perspective paper [1] and the review paper [2] reinforce the importance and relevance of this multidisciplinary field and exemplify the diversity of its applications and mathematical tools.

This Focus Issue brings together contributions from the field of adaptive (co-evolutionary) dynamical networks. It focuses on multidisciplinary applications as well as on the development of theoretical tools and advanced numerical studies.

[1] Sawicki, J.et al. (2023). Perspectives on adaptive dynamical systems, Chaos 33, 071501.
[2] Berner, R., Gross, T., Kuehn, C., Kurths, J., & Yanchuk, S. (2023). Adaptive Dynamical Networks. https://arxiv.org/abs/2304.05652v1

Topics covered include, but are not limited to:

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Guest Editors

Serhiy Yanchuk (University College Cork, Ireland; Potsdam Institute for Climate Impact Research (PIK), Germany)

Erik Martens (Centre for Mathematical Sciences, Lund University, Sweden)

Christian Kuehn (Faculty of Mathematics, Technical University of Munich, Germany)

Jürgen Kurths (Department of Physics, Humboldt Universität zu Berlin, Germany; Potsdam Institute for Climate Impact Research (PIK), Germany)