2026-04-21T16:03:49Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/67772025-06-17T16:16:29Zcom_20.500.11984_473col_20.500.11984_478

Arruti Romero, Asier Agote San Sebastian, Anartz Alberdi Esuain, Borja Aizpuru, Iosu 2024-11-14T14:40:40Z 2024-11-14T14:40:40Z 2024 2644-1314 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178265 https://hdl.handle.net/20.500.11984/6777 This paper 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 stateof-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. eng Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2024 IEEE open source software Data-driven methods Machine learning Artificial Intelligence Power magnetics Power ferrites ODS 7 Energía asequible y no contaminante ODS 9 Industria, innovación e infraestructura ODS 12 Producción y consumo responsables MagNet challenge for data-driven power magnetics modeling http://purl.org/coar/resource_type/c_6501