@inproceedings{c1151831ebd34a3480c057a358d21806,
title = "Transfer Learning Methods for Magnetic Core Loss Modeling",
abstract = "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 that a neural network trained for modeling the core losses of a certain group of magnetic materials under certain excitations can be retrained to model the core loss of other magnetic materials under similar excitations, with a reduced set of measurement data. This approach can also be applied to model the core loss of the same magnetic material under different excitations. Experiments are designed and compared to verify the effectiveness of material-to-material transfer learning and waveform-to-waveform transfer learning.",
keywords = "Core loss, Data-driven method, Machine learning, Neural network, Power magnetics, Transfer learning",
author = "Evan Dogariu and Haoran Li and {Serrano Lopez}, Diego and Shukai Wang and Min Luo and Minjie Chen",
note = "Funding Information: ACKNOWLEDGEMENTS This work was supported by ARPA-E under the DIFFERENTIATE program. We also gratefully acknowledge financial support from the Schmidt DataX Fund at Princeton University made possible through a major gift from the Schmidt Futures Foundation. This project has also received support from Dartmouth College and Plexim GmbH. Publisher Copyright: {\textcopyright} 2021 IEEE.; 22nd IEEE Workshop on Control and Modelling of Power Electronics, COMPEL 2021 ; Conference date: 02-11-2021 Through 05-11-2021",
year = "2021",
doi = "10.1109/COMPEL52922.2021.9646065",
language = "English (US)",
series = "2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics, COMPEL 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE 22nd Workshop on Control and Modelling of Power Electronics, COMPEL 2021",
address = "United States",
}