  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
