Power converters, artificial neural networks, finite element analysis, inductors, machine learning, magnetic devices, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

This paper analyzes the potential of Artificial Neural Networks (ANNs) for the modeling and optimization of magnetic components and, specifically, inductors. After reviewing the basic properties of ANNs, several potential modeling and design workflows are presented. A hybrid method, which combines the accuracy of 3D Finite Element Method (FEM) and the low computational cost of ANNs, is selected and implemented. All relevant effects are considered (3D magnetic and thermal field patterns, detailed core loss data, winding proximity losses, coupled loss-thermal model, etc.) and the implemented model is extremely versatile ($30$ input and $40$ output variables). The proposed ANN-based model can compute $50^{\prime}000$ designs per second with less than $3 \%$ deviation with respect to 3D FEM simulations. Finally, the inductor of a $2$ kW DC-DC buck converter is optimized with the ANN-based workflow. From the Pareto fronts, a design is selected, measured, and successfully compared with the results obtained with the ANNs. The implementation (source code and data) of the proposed workflow is available under an open-source license.