🎯 Motivation and Objectives

The analysis and control of complex dynamical systems remain fundamental challenges in modern engineering. As systems grow in complexity—spanning energy networks, autonomous systems, cyber-physical systems, and beyond—traditional model-based approaches often struggle to scale or to capture the full richness of system behavior. Simultaneously, advances in machine learning, optimization theory, and computational methods have opened new avenues for addressing these challenges.

This special session aims to bring together researchers working at the intersection of data-driven methods, optimization-based techniques, and dynamical systems theory. The session will explore how modern computational tools—including neural networks, sum-of-squares optimization, and reinforcement learning—can be integrated with classical stability and control frameworks to address problems in safety verification, stability analysis, and controller synthesis for complex systems.

📋 Topics of Interest

🎤 Session Format

📊 Session Abstract

This session focuses on recent advances in artificial intelligence and data-driven intelligent systems, emphasizing methodological innovations, practical deployments, and interdisciplinary applications. The session will cover machine learning models for prediction and optimization, AI-enabled decision-support systems, blockchain-integrated intelligent platforms, and secure computing architectures for healthcare, finance, and smart infrastructure.

Particular attention is given to explainability, scalability, and ethical considerations in real-world AI systems. Through empirical studies, case-based implementations, and comparative modeling approaches, the session aims to provide researchers and practitioners with insights into how contemporary AI techniques translate from theoretical constructs to impactful, deployable solutions across diverse domains.

🎯 Key Focus Areas