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

Alan Yang, Jie Xiong, Maxim Raginsky, Elyse Rosenbaum
Proceedings of The 4th Annual Learning for Dynamics and Control Conference, PMLR 168:663-675, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v168-yang22b, title = {Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits}, author = {Yang, Alan and Xiong, Jie and Raginsky, Maxim and Rosenbaum, Elyse}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference}, pages = {663--675}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/yang22b/yang22b.pdf}, url = {https://proceedings.mlr.press/v168/yang22b.html}, 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.} }
Endnote
%0 Conference Paper %T Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits %A Alan Yang %A Jie Xiong %A Maxim Raginsky %A Elyse Rosenbaum %B Proceedings of The 4th Annual Learning for Dynamics and Control Conference %C Proceedings of Machine Learning Research %D 2022 %E Roya Firoozi %E Negar Mehr %E Esen Yel %E Rika Antonova %E Jeannette Bohg %E Mac Schwager %E Mykel Kochenderfer %F pmlr-v168-yang22b %I PMLR %P 663--675 %U https://proceedings.mlr.press/v168/yang22b.html %V 168 %X 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.
APA
Yang, A., Xiong, J., Raginsky, M. & Rosenbaum, E.. (2022). Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits. Proceedings of The 4th Annual Learning for Dynamics and Control Conference, in Proceedings of Machine Learning Research 168:663-675 Available from https://proceedings.mlr.press/v168/yang22b.html.

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