Hybrid Time-Domain Behavior Model Based on Neural Differential Equations and RNNs

Nonlinear dynamics system identification is crucial for circuit emulation. Traditional continuous-time domain modeling approaches have limitations in fitting capability and computational efficiency when used for modeling circuit IPs and devicethis http URLpaper presents a novel continuous-time domain hybrid modeling paradigm. It integrates neural network differential models with recurrent neural networks (RNNs), creating NODE-RNN and NCDE-RNN models based on neural ordinary differential equations (NODE) and neural controlled differential equations (NCDE),this http URLanalysis shows that this hybrid model has mathematical advantages in event-driven dynamic mutation response and gradient propagation stability. Validation using real data from PIN diodes in high-power microwave environments shows NCDE-RNN improves fitting accuracy by 33\% over traditional NCDE, and NODE-RNN by 24\% over CTRNN, especially in capturing nonlinear memorythis http URLmodel has been successfully deployed in Verilog-A and validated through circuit emulation, confirming its compatibility with existing platforms and practicalthis http URLhybrid dynamics paradigm, by restructuring the neural differential equation solution path, offers new ideas for high-precision circuit time-domain modeling and is significant for complex nonlinear circuit system modeling.

@article{chang2025_2503.22313,
  title={ Hybrid Time-Domain Behavior Model Based on Neural Differential Equations and RNNs },
  author={ Zenghui Chang and Yang Zhang and Hu Tan and Hong Cai Chen },
  journal={arXiv preprint arXiv:2503.22313},
  year={ 2025 }
}