Electrical circuits are present in a variety of technologies, making their design an important part of computer aided engineering. The growing number of tunable parameters that affect the final design leads to a need for new approaches of quantifying their impact. Machine learning may play a key role in this regard, however current approaches often make suboptimal use of existing knowledge about the system at hand. In terms of circuits, their description via modified nodal analysis is well-understood. This particular formulation leads to systems of differential-algebraic equations (DAEs) which bring with them a number of peculiarities, e.g. hidden constraints that the solution needs to fulfill. We aim to use the recently introduced dissection concept for DAEs that can decouple a given system into ordinary differential equations, only depending on differential variables, and purely algebraic equations that describe the relations between differential and algebraic variables. The idea then is to only learn the differential variables and reconstruct the algebraic ones using the relations from the decoupling. This approach guarantees that the algebraic constraints are fulfilled up to the accuracy of the nonlinear system solver, which represents the main benefit highlighted in this article.

翻译：电路广泛应用于各类技术中，其设计是计算机辅助工程的重要环节。影响最终设计的可调参数日益增多，亟需新方法来量化其影响。机器学习可能在此方面发挥关键作用，但当前方法往往未能充分利用对当前系统的既有知识。就电路而言，通过修正节点分析法对其描述已得到充分理解。这种特定表述导致微分代数方程组（DAEs）的产生，该类方程带来诸多特性，例如解须满足的隐式约束。我们旨在利用近期提出的DAEs解耦概念，该概念可将给定系统解耦为仅依赖微分变量的常微分方程，以及描述微分变量与代数变量关系的纯代数方程。核心思路是仅学习微分变量，并利用解耦关系重构代数变量。该方法可确保代数约束在非线性系统求解器精度范围内得到满足，这正是本文强调的主要优势。