  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.
