A Physics-Informed Neural Network Method to Approximate Homogeneous Lyapunov Functions

This paper applies the physics-informed neural network approach to approximate globally-defined Lyapunov functions and stabilizing controls for homogeneous dynamical systems. The advantage of this class of systems is that all analysis and design can be made locally on a suitably defined unit sphere, which corresponds perfectly to the applicability conditions of neural networks.