Transfer Learning Methods for Magnetic Core Loss Modeling

Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point to initiate the model for another. This paper applies transfer learning to magnetic core loss modeling to reduce the amount of data needed to achieve improved performance for a variety of tasks. Leveraging a recently developed magnetic core loss dataset - MagNet - we demonstrate t...