@inproceedings{b2d9e7080c714f0b99159fad226b46ce,
title = "MagNetX: Foundation Neural Network Models for Simulating Power Magnetics in Transient",
abstract = "This paper introduces a foundation neural network framework for modeling power magnetics in transient, based on MagNetX1 - a new extension of the MagNet database which includes extensive measurement data in transient. Provided with flux density B(t) and field intensity H(t) waveforms, the model uses partial memories and the next-state flux density excitation to predict the response of the field intensity in the next time step. The model is in time domain and is frequency independent. An example sequence-to-scalar LSTM neural network was designed, trained, and tested. This modeling framework can greatly enhance the modeling and design of power magnetics operating in transient condition, such as in PFCs and power amplifiers.",
keywords = "data-driven method, hysteresis loop, machine learning, neural network, power magnetics, transformer",
author = "Shukai Wang and Hyukjae Kwon and Haoran Li and Youssef Elasser and Kang, {Gyeong Gu} and Daniel Zhou and Davit Grigoryan and Minjie Chen",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025 ; Conference date: 16-03-2025 Through 20-03-2025",
year = "2025",
doi = "10.1109/APEC48143.2025.10977420",
language = "English (US)",
series = "Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2438--2445",
booktitle = "APEC 2025 - 14th Annual IEEE Applied Power Electronics Conference and Exposition",
address = "United States",
}