MagNet Challenge for Data-Driven Power Magnetics Modeling

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 Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2025-01-01
DOI
https://doi.org/10.3929/ethz-b-000739225
Primary Topic
Magnetic Properties and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

MagNet Challenge for Data-Driven Power Magnetics Modeling

Repository for Publications and Research Data (ETH Zurich)
Magnetic Properties and Applications
article

MagNet Challenge for Data-Driven Power Magnetics Modeling

article en

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.

Repository for Publications and Research Data (ETH Zurich)
Openalex Percentile: Top 63%
Magnetic Properties and Applications
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MagNet Challenge for Data-Driven Power Magnetics Modeling · Repository for Publications and Research Data (ETH Zurich) (2025) | TGRS Research Map | TGRS