How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics-Reference-Cited by-同舟云学术

How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics

Published:2023-12 Issue:12 Volume:38 Page:15829-15853
ISSN:0885-8993
Container-title:IEEE Transactions on Power Electronics
language:
Short-container-title:IEEE Trans. Power Electron.

Author:

Li Haoran¹^ORCID,Serrano Diego¹^ORCID,Guillod Thomas²^ORCID,Wang Shukai¹^ORCID,Dogariu Evan¹^ORCID,Nadler Andrew²,Luo Min³^ORCID,Bansal Vineet¹^ORCID,Jha Niraj K.¹^ORCID,Chen Yuxin⁴^ORCID,Sullivan Charles R.²^ORCID,Chen Minjie¹^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ, USA

2. Dartmouth College, Hanover, NH, USA

3. BASiC Semi, Ekarus Engines GmbH, Essingen, Germany

4. Department of Statistics and Data Science, Wharton School, University of Pennsylvania, Philadelphia, PA, USA

Funder

ARPA-E Differentiate Project

Schmidt DataX Fund at Princeton University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/63/10297410/10232863.pdf?arnumber=10232863

Reference57 articles.

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2. Influence of Magnetic History and Accommodation on Hysteresis Loss for Arbitrary Core Excitations

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4. A simplified mirror transformer dc-bias injection method for power magnetics characterization;wang;Proc IEEE Appl Power Electron Conf Expo,0

5. on the law of hysteresis;steinmetz;Proceedings of the IEEE,1984

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