Overcoming the curse of dimensionality for approximating Lyapunov functions with deep neural networks under a small-gain condition (2001.08423v3)

Abstract: We propose a deep neural network architecture for storing approximate Lyapunov functions of systems of ordinary differential equations. Under a small-gain condition on the system, the number of neurons needed for an approximation of a Lyapunov function with fixed accuracy grows only polynomially in the state dimension, i.e., the proposed approach is able to overcome the curse of dimensionality.