This paper proposes a method of transferring physical continuous and switching/converter circuits working in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) to graph representation, independent of the connection or the number of circuit components, so that machine learning (ML) algorithms and applications can be easily applied. Such methodology is generalized and is applicable to circuits with any number of switches, components, sources and loads, and can be useful in applications such as artificial intelligence (AI) based circuit design automation, layout optimization, circuit synthesis and performance monitoring and control. The proposed circuit representation and feature extraction methodology is applied to seven types of continuous circuits, ranging from second to fourth order and it is also applied to three of the most common converters (Buck, Boost, and Buck-boost) operating in CCM or DCM. A classifier ML task can easily differentiate between circuit types as well as their mode of operation, showing classification accuracy of 97.37% in continuous circuits and 100% in switching circuits.

中文翻译：

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使用同构图神经网络将 CCM 和 DCM 中的连续/开关电路综合映射到机器学习域

本文提出了一种将工作在连续导通模式 (CCM) 和断续导通模式 (DCM) 的物理连续和开关/转换器电路转换为图形表示的方法，与电路组件的连接或数量无关，以便机器学习 (ML) ) 算法和应用程序可以很容易地应用。这种方法是通用的，适用于具有任意数量的开关、组件、源和负载的电路，并且可用于基于人工智能 (AI) 的电路设计自动化、布局优化、电路综合以及性能监测和控制等应用。所提出的电路表示和特征提取方法适用于七种类型的连续电路，范围从二阶到四阶，它也适用于以 CCM 或 DCM 运行的三种最常见的转换器（降压、升压和降压-升压）。分类器 ML 任务可以轻松区分电路类型及其操作模式，连续电路的分类准确率为 97.37%，开关电路的分类准确率为 100%。