Introduction
The study of nonlinear dynamical systems has long been a cornerstone of applied mathematics, physics, engineering, and biological sciences. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), new avenues have emerged for modeling, analyzing, and controlling complex dynamical behaviors. This special issue aims to bridge the gap between classical nonlinear system theory and modern AI/ML methodologies, fostering interdisciplinary research that enhances predictive accuracy, computational efficiency, and system understanding.
We invite high-quality research contributions, review articles, and case studies that explore the integration of AI/ML techniques with nonlinear dynamical system theory. The focus is on innovative approaches, algorithms, and applications that demonstrate the synergy between data-driven intelligence and nonlinear dynamics.
Keywords
AL, ML, Neural networks, Fractional calculus, Integral transform, nonlinear analysis
Sub-topics
- AI- and ML-based modeling of nonlinear dynamical systems
- Data-driven system identification and control
- Neural networks for stability and bifurcation analysis
- Reinforcement learning for nonlinear control and optimization
- Hybrid modeling combining differential equations with ML architectures
- Fractional-order and chaotic systems with AI/ML frameworks
- Predictive modeling in biological, ecological, and socio-economic nonlinear systems
- Explainable AI methods for nonlinear dynamics
- Reduced-order modeling and surrogate modeling using AI/ML
- Applications in robotics, fluid dynamics, climate systems, finance, and power systems
