MagNet Challenge for Data-Driven Power Magnetics Modeling


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Date

2025

Publication Type

Journal Article

ETH Bibliography

yes

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Abstract

This article summarizes the main results and contributions of the MagNet Challenge 2023, an open-source research initiative for data-driven modeling of power magnetic materials. The MagNet Challenge has (1) advanced the state-of-the-art in power magnetics modeling; (2) set up examples for fostering an open-source and transparent research community; (3) developed useful guidelines and practical rules for conducting data-driven research in power electronics; and (4) provided a fair performance benchmark leading to insights on the most promising future research directions. The competition yielded a collection of publicly disclosed software algorithms and tools designed to capture the distinct loss characteristics of power magnetic materials, which are mostly open-sourced. We have attempted to bridge power electronics domain knowledge with state-of-the-art advancements in artificial intelligence, machine learning, pattern recognition, and signal processing. The MagNet Challenge has greatly improved the accuracy and reduced the size of data-driven power magnetic material models. The models and tools created for various materials were meticulously documented and shared within the broader power electronics community.

Publication status

published

Editor

Book title

Volume

6

Pages / Article No.

883 - 898

Publisher

IEEE

Event

Edition / version

Methods

Software

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Subject

Artificial intelligence; data-driven methods; machine learning; open-source; power magnetics; power ferrites

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Notes

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