Neural Ordinary Differential Equation Models of Circuits: Capabilities and Pitfalls

Jie Xiong, Alan Yang, Maxim Raginsky, Elyse Rosenbaum

Research output: Contribution to journalArticlepeer-review

Abstract

This work advances the application of neural ordinary differential equations (ODEs) to circuit modeling. Prior works primarily utilized the recurrent neural network (RNN), which is a specific type of neural ODE. In this work, the capability of neural ODEs to represent different types of circuits is studied. Stability conditions are presented, both for neural ODEs in a standalone configuration and for neural ODEs with feedback connections, and practical techniques to impose the stability constraints during training are demonstrated. Based on the theoretical and experimental results, this work provides guidance as to when and how an accurate and stable neural ODE circuit model can be generated.

Original languageEnglish (US)
Pages (from-to)4869-4884
Number of pages16
JournalIEEE Transactions on Microwave Theory and Techniques
Volume70
Issue number11
DOIs
StatePublished - Nov 1 2022
Externally publishedYes

Keywords

  • Behavioral modeling
  • neural ordinary differential equation (ODE)
  • nonlinear circuit
  • recurrent neural network (RNN)
  • stability

ASJC Scopus subject areas

  • Radiation
  • Condensed Matter Physics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Neural Ordinary Differential Equation Models of Circuits: Capabilities and Pitfalls'. Together they form a unique fingerprint.

Cite this