输入-状态稳定的神经元常微分方程及其在电路瞬态建模中的应用
Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits
摘要：
提出了一类由可证明输入-状态稳定的连续时间递归神经网络参数化的神经元常微分方程。该模型的动态定义的建设是输入到状态稳定（ISS）相对于一个ISS-李雅普诺夫函数，学习联合与动态。我们使用所提出的方法来学习便宜的模拟行为模型的电子电路，可以准确地再现各种数字和模拟电路的行为时，由商业电路模拟器模拟，即使在训练过程中没有遇到的电路组件互连。我们还证明了学习ISS保持扰动的动态建模退化的影响，由于电路老化的可行性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
