Data-Driven Nonlinear Dynamics and Complex Networks: Inference, Prediction and Control

Complex dynamical systems generate rich nonlinear behaviour across science, engineering, biology and society, while increasingly large observational and computational datasets create new opportunities for their inference, prediction, and control. This Collection will bring together recent mathematical advances at the interface of nonlinear dynamics, complex networks, data-driven and AI-assisted modelling. Emphasis will be placed on methods for extracting dynamical structure from data, reconstructing interactions and networks, predicting transitions and extreme behaviour, quantifying uncertainty, and designing effective control strategies. Topics of interest include nonlinear time-series analysis, network inference, synchronisation and collective dynamics, stochastic dynamical systems, chaos detection and prediction, reduced-order modelling, physics-informed and hybrid methods, and control of networked systems. Contributions combining rigorous mathematical development with computational or interdisciplinary applications in areas such as neuroscience, epidemiology, engineering, energy, climate and socio-economic systems are particularly welcome. The Collection aims to strengthen connections between classical dynamical-systems theory and modern data-driven applied mathematics, with a particular focus on advancing the themes of inference, prediction, and control across complex systems.

Keywords: Nonlinear dynamics; Complex networks; Data-driven modelling; Network inference; Chaos; Scientific machine learning; Stochastic dynamical systems; Synchronisation; Prediction; Control