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This paper proposes a class of neural ordinary differential equationsparametrized by provably input-to-state stable continuous-time recurrent neuralnetworks. The model dynamics are defined by construction to be input-to-statestable (ISS) with respect to an ISS-Lyapunov function that is learned jointlywith the dynamics. We use the proposed method to learn cheap-to-simulatebehavioral models for electronic circuits that can accurately reproduce thebehavior of various digital and analog circuits when simulated by a commercialcircuit simulator, even when interconnected with circuit components notencountered during training. We also demonstrate the feasibility of learningISS-preserving perturbations to the dynamics for modeling degradation effectsdue to circuit aging.

Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits