Title
MagNet challenge for data-driven power magnetics modelingAuthor (from another institution)
Version
http://purl.org/coar/version/c_ab4af688f83e57aa
Rights
© 2024 IEEEAccess
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.1109/OJPEL.2024.3469916Published at
IEEE Open Journal of Power Electronics Publisher
IEEEKeywords
open source software
Data-driven methods
Machine learning
Artificial Intelligence ... [+]
Data-driven methods
Machine learning
Artificial Intelligence ... [+]
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 [-]
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 [-]
xmlui.dri2xhtml.METS-1.0.item-unesco-tesauro
http://vocabularies.unesco.org/thesaurus/concept3052xmlui.dri2xhtml.METS-1.0.item-unesco-clasificacion
http://skos.um.es/unesco6/3304Abstract
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 ... [+]
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. [-]
Collections
- Articles - Engineering [677]
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