System Theoretic

System theory provides a mathematical framework for analyzing and designing complex systems, focusing on understanding their behavior and interactions. Current research emphasizes applying system-theoretic principles to improve model order reduction in deep learning, particularly for control systems and intelligent systems engineering, often leveraging techniques like modal regularization and interval analysis to enhance model accuracy and efficiency. This approach is crucial for addressing challenges in areas like autonomous driving, where managing uncertainties and ensuring system dependability are paramount, and for developing more robust and interpretable machine learning algorithms inspired by human cognition.