Data-Driven Models and Analysis of Complex Systems

The 2021 Nobel Prize in Physics placed Complex Systems under the spotlight. This includes biological, social, ecological, atmospheric, financial, or technological systems among many other. It also emphasizes the importance of studying natural and man-made systems to understand their emerging behaviors, such as synchronization, spatiotemporal chaos, extreme events, and tipping points to name a few. These behaviors have been subject to research due to the fascinating spatiotemporal patterns they create; possible catastrophic consequences; or the universality that bounds them. Despite coming from disparate systems having a broad range of temporal and spatial scales, these behaviors require no external or central controller but have a strong dependence on the structure of their interactions – typically represented as a network. Furthermore, with the advent of large and more precise data recordings – often big data –, novelty models, methods, and machine learning techniques have stepped up to produce innovative studies of complex systems and their emergent behaviors.

This Focus Issue welcomes manuscripts that present new insights about Complex Systems derived from computational or data-driven models, based on data analysis, or novel mathematical approaches. We promote the development of new methods to analyze data which can help characterize complex systems in general, collective behaviors, or network structures. We also encourage submissions that include software packages which allow the reproduction and implementation of the reported results.

Topics covered include, but are not limited to:

Guest Editors

Johann H. Martínez (Universidad Complutense de Madrid, Spain)

Klaus Lehnertz (University of Bonn, Germany)

Nicolás Rubido (University of Aberdeen, United Kingdom)