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：电路存在于多种技术中，因此其设计成为计算机辅助工程的重要组成部分。影响最终设计的可调参数数量不断增加，从而需要新的方法来量化其影响。机器学习在这方面可能发挥关键作用，然而当前方法往往未能充分利用关于现有系统的知识。就电路而言，通过改进节点分析对其进行的描述已得到充分理解。这种特定公式导致微分代数方程组（DAEs）的产生，并带来了一系列特性，例如解需要满足的隐藏约束。我们旨在利用最近提出的DAEs解耦概念，该概念可将给定系统解耦为仅依赖于微分变量的常微分方程，以及描述微分变量与代数变量之间关系的纯代数方程。其思路是仅学习微分变量，并利用解耦得到的关系重建代数变量。该方法保证了代数约束能够满足非线性系统求解器的精度，这正是本文强调的主要优势。