Active Excitation

Active excitation studies the response of dynamical systems to external stimuli, aiming to understand and predict system behavior under various conditions. Current research focuses on developing efficient and accurate models, including physics-informed neural networks and other deep learning architectures, to analyze data from diverse systems ranging from mechanical structures and robotic systems to biological tissues and neural networks. These advancements enable faster model training, improved prediction accuracy, and the ability to handle complex, high-dimensional data, impacting fields like structural health monitoring, medical imaging, and signal processing. The ultimate goal is to extract meaningful information from system responses, leading to better control, diagnosis, and understanding of complex phenomena.