论文标题
输入到国家稳定的神经普通微分方程，并在电路的瞬态建模中应用
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
论文作者
论文摘要
本文提出了一类神经常规微分方程，该方程是通过可证明的输入到状态稳定的连续时间复发网络参数化的。模型动力学是由构造定义的，该构造是根据与动力学共同学习的ISS-Lyapunov功能相对于ISS-Lyapunov函数的。我们使用所提出的方法来学习廉价到模拟的电子电路的行为模型，这些电路可以准确地重现由商用电路模拟器模拟时，即使在训练过程中未遇到的电路组件相互联系时，也可以准确地重现各种数字和模拟电路的行为。我们还证明了学习ISS保护扰动的可行性，以模拟由于电路老化而导致的降解效应的动力学。
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.
