System Dynamic

System dynamics research focuses on modeling and predicting the behavior of complex systems over time, aiming to understand their underlying mechanisms and optimize their performance. Current research emphasizes developing machine learning models, including recurrent neural networks, graph neural ODEs, and transformer-based architectures, to learn system dynamics from data, often incorporating physical constraints or prior knowledge for improved accuracy and generalizability. This field is crucial for addressing challenges across diverse domains, from robotics and control systems to hydrology and healthcare, by enabling more accurate predictions, efficient control strategies, and improved understanding of complex interactions. The development of robust and interpretable models is a key focus, particularly in applications where safety and reliability are paramount.