Physics-Informed Neural Networks for Magnetostatic Problems on Axisymmetric Transformer Geometries
Philipp Brendel, Vlad Medvedev, Andreas Roßkopf
Abstract
Physics-Informed Neural Networks (PINNs) have shown their potential for solving direct problems in many engineering domains including electromagnetics. We employ fully connected and convolutional PINNs in order to predict the magnetic vector potential and resulting inductances and coupling for axisymmetric transformer geometries commonly used in inductive power systems. Both approaches are compared quantitatively and validated against reference solutions obtained from a numerical solver. Fully connected PINNs tend to train faster and more accurate for single geometries, whereas convolutional PINNs show an outperformance in terms of their generalization capability and are able to predict inductances and coupling for a wide range of transformer geometries accurately in a matter of milliseconds. The combination of high accuracy and fast inference paves the way for PINN-based topology optimization in the field of power electronics.