How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics
Haoran Li, Diego Serrano, Thomas Guillod, Shukai Wang, Evan Dogariu, Andrew B. Nadler, Min Luo, Vineet Bansal, Niraj K. Jha, Yuxin Chen, Charles R. Sullivan, Minjie Chen
Abstract
This paper applies machine learning to power magnetics modeling. We first introduce an open-source database – MagNet – which hosts a large amount of experimentally measured excitation data for many materials across a variety of operating conditions, consisting of more than 500,000 data points in its current state. The processes for data acquisition and data quality control are explained. We then demonstrate a few neural network-based power magnetics modeling tools for modeling the core losses and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$B$</tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H$</tex-math></inline-formula> loops. The neural network allows multiple factors that may influence the magnetic characteristics to be modeled in a unified framework, where the non-linear behaviors are captured with high accuracy and high generality. Neural network models are found to be effective in compressing the measurement data and predicting the material characteristics, paving the way for “neural networks as datasheets” to assist power magnetics design. Transfer learning is applied to the training of neural network models to further reduce the data size requirement while maintaining sufficient model accuracy.