This paper proposes representation and feature extraction for DC-DC converter circuits to ML, and validating the methodology by apply a classifier ML task. By representing converters as graphs and applying GNNs, a classifier is able to discriminate between converter connections and operating modes. This is an enabling tool for circuit design, model predictive control and automated layout applications.
