MagNet: An Open-Source Database for Data-Driven Magnetic Core Loss Modeling-Reference-Cited by-同舟云学术

MagNet: An Open-Source Database for Data-Driven Magnetic Core Loss Modeling

Published:2022-03-20 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Applied Power Electronics Conference and Exposition (APEC)
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Author:

Li Haoran¹,Serrano Diego¹,Guillod Thomas²,Dogariu Evan¹,Nadler Andrew²,Wang Shukai¹,Luo Min³,Bansal Vineet¹,Chen Yuxin¹,Sullivan Charles R.²,Chen Minjie¹

Affiliation:

1. Princeton University,Princeton,NJ,United States

2. Dartmouth College,Hanover,NH,United States

3. Plexim GmbH,Zurich,Switzerland

Funder

Princeton University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9773364/9773365/09773372.pdf?arnumber=9773372

Reference33 articles.

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3. Transfer Learning Methods for Data-Driven Magnetic Core Loss Modeling;dogariu;IEEE Work-shop on Control and Modeling for Power Electronics (COMPEL),2021

4. Optuna

5. On the Law of Hysteresis;steinmetz;Proceedings of the IEEE,0

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1. High-Frequency Core Loss Modeling Based on Knowledge-Aware Artificial Neural Network;IEEE Transactions on Power Electronics;2024-02

2. MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation;IEEE Transactions on Power Electronics;2023-12

3. How MagNet: Machine Learning Framework for Modeling Power Magnetic Material Characteristics;IEEE Transactions on Power Electronics;2023-12

4. A Method and a Three-Source Converter for Medium-Frequency Magnetic Elements Losses Measurement;IEEE Transactions on Industrial Electronics;2023-12

5. Why MagNet: Quantifying the Complexity of Modeling Power Magnetic Material Characteristics;IEEE Transactions on Power Electronics;2023-11