  This paper proposes a class of neural ordinary differential equations
parametrized by provably input-to-state stable continuous-time recurrent neural
networks. The model dynamics are defined by construction to be input-to-state
stable (ISS) with respect to an ISS-Lyapunov function that is learned jointly
with the dynamics. We use the proposed method to learn cheap-to-simulate
behavioral models for electronic circuits that can accurately reproduce the
behavior of various digital and analog circuits when simulated by a commercial
circuit simulator, even when interconnected with circuit components not
encountered during training. We also demonstrate the feasibility of learning
ISS-preserving perturbations to the dynamics for modeling degradation effects
due to circuit aging.
