TY - JOUR
T1 - MagNet Challenge for Data-Driven Power Magnetics Modeling
AU - Chen, Minjie
AU - Li, Zhengzhao
AU - Mirzadarani, Reza
AU - Liu, Ruijun
AU - Wang, Lu
AU - Luo, Tianming
AU - Lyu, Dingsihao
AU - Niasar, Mohamad Ghaffarian
AU - Qin, Zian
AU - More Authors, null
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Data-Driven Methods
KW - Machine Learning
KW - Open-Source
KW - Power Ferrites
KW - Power Magnetics
UR - http://www.scopus.com/inward/record.url?scp=85206320836&partnerID=8YFLogxK
U2 - 10.1109/OJPEL.2024.3469916
DO - 10.1109/OJPEL.2024.3469916
M3 - Article
AN - SCOPUS:85206320836
SN - 2644-1314
VL - 6
SP - 883
EP - 898
JO - IEEE Open Journal of Power Electronics
JF - IEEE Open Journal of Power Electronics
ER -