Physics-Informed Machine Learning for Characterizing System Stability

The introduction of a physics-informed machine learning method for characterizing system stability marks a significant advancement in the field of dynamical systems, particularly in aerospace applications. Traditionally, determining the stability region of such systems has been challenging, as it often requires explicit knowledge of governing equations. The new method, which infers a Lyapunov function from trajectory data, allows for the estimation of stability regions without this prerequisite knowledge. This approach not only simplifies the process but also enhances the reliability of ensuring that all state trajectories converge to a desired equilibrium. The importance of stability regions cannot be overstated, as they are essential for the safe operation of complex systems. By leveraging machine learning, this method opens new avenues for research and application in fields where system dynamics are critical.