Nonlinear Systems from Classical and Machine-Learning Perspectives: Modeling, Dynamics, and Applications

Dear Colleagues,

Nonlinear phenomena are intrinsic to a wide range of physical systems and play a central role in contemporary science and engineering. From electronic circuits and communication infrastructures to biological processes and neural information processing, nonlinear dynamics give rise to rich behaviors such as multistability, bifurcation, chaos, and hidden attractors. These features not only challenge conventional analytical techniques but also create new opportunities for modeling, control, and technological innovation.

Recent advances in data-driven methods—particularly machine learning (ML) and artificial intelligence (AI)—have opened new pathways for investigating nonlinear systems. When combined with classical analytical and numerical approaches, these emerging techniques enable improved system identification, prediction, and real-time implementation, even in highly complex or partially observed environments.

This Special Issue aims to bring together theoretical, computational, and experimental contributions that advance the understanding of nonlinear systems from both classical and ML-based perspectives. Topics of interest include (but are not limited to) nonlinear and chaotic dynamics, complex and networked systems, fractional-order models, control and synchronization strategies, and physics-informed machine-learning frameworks. Contributions addressing practical implementations and interdisciplinary applications are particularly encouraged.

Dr. Viet-Thanh Pham
Prof. Dr. Jesus M. Munoz-Pacheco
Dr. Andrei Velichko
Guest Editors

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• nonlinear dynamics
• chaos
• complex systems
• system identification
• machine learning in physics
• control
• synchronization
• fractional-order systems
• applications