A Heterogeneous Graph Framework for ML Applications in Electric Circuits

Machine learning (ML) has the potential to revolutionize the design and optimization of electronic systems, but mainly requires high-quality and diverse datasets that capture the features and performance of various electrical circuits. Previously, there were no generic method applicable to any electric circuit whether continuous circuit like resonant LC circuits or switching circuits like buck, boost and buck-boost that allows circuit connections and components separate representation when circuits are fed to ML models. Moreover, all other methods and models are inefficiently built to process either limited complexity circuits, i.e. limited to a certain circuit order, or use a fixed circuit topology so that the generated dataset is of a definite size. In this paper, a novel circuit representation that is based on heterogeneous graph is presented, which is a graphical representation of the energy flow in a physical system including electrical, mechanical and even chemical interactions. Moreover, heterogeneous graph neural network (Hetero-GNN) is applied to the proposed representation of heterogeneous graphs representing electric circuits, allowing for the different ML tasks on different electric circuit and converter applications.