Predicting the B-H Loops of Power Magnetics with Transformer-based Encoder-Projector-Decoder Neural Network Architecture

Author:

Li Haoran1,Serrano Diego1,Wang Shukai1,Guillod Thomas2,Luo Min3,Chen Minjie1

Affiliation:

1. Princeton University,Princeton,NJ,United States

2. Dartmouth College,Hanover,NH,United States

3. Ekarus Engines GmbH,Essingen,Germany

Funder

DOE ARPA-E DIFFERENTIATE

Schmidt DataX Fund at Princeton University

Publisher

IEEE

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Knowledge and data fusion-driven dynamical modeling approach for structures with hysteresis-affected uncertain boundaries;Nonlinear Dynamics;2024-08-23

2. Power Inductor Design Optimization with Data-Driven Magnetic Loss Models;2024 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM);2024-06-19

3. Deep learning model for enhanced power loss prediction in the frequency domain for magnetic materials;IET Power Electronics;2024-05-26

4. Investigating the Mutual Impact of Waveform, Temperature, and Dc-Bias on Magnetic Core Loss using Neural Network Models;2024 IEEE Applied Power Electronics Conference and Exposition (APEC);2024-02-25

5. Multi-Material Power Magnetics Modeling with a Modular and Scalable Machine Learning Framework;2024 IEEE Applied Power Electronics Conference and Exposition (APEC);2024-02-25

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