Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
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.
Keywords
Cite
@article{arxiv.2202.06453,
title = {Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits},
author = {Alan Yang and Jie Xiong and Maxim Raginsky and Elyse Rosenbaum},
journal= {arXiv preprint arXiv:2202.06453},
year = {2022}
}