Autonomous Dynamical System

Autonomous dynamical systems research focuses on understanding and controlling systems whose behavior is governed by internal dynamics, independent of external forcing. Current efforts concentrate on developing robust methods for system identification, often employing machine learning techniques like neural networks and sparse identification of nonlinear dynamics (SINDy), to model complex, noisy, and non-stationary systems. These advancements are crucial for improving the stability and safety of applications ranging from robotics and control systems to biological modeling and forecasting, enabling more accurate predictions and more reliable control strategies. Furthermore, research is actively exploring the use of Lyapunov functions and other stability analysis techniques to ensure the safety and reliability of learned models.