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arXiv · 2202.06453

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

Alan Yang·Jie Xiong·Maxim Raginsky·Elyse Rosenbaum
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Abstract

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.

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Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum. 2022-02-14. Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits. https://arxiv.org/abs/2202.06453

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