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MagNet-AI: Neural Network as Datasheet for Magnetics Modeling and Material Recommendation
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  • Minjie Chen ,
  • Haoran Li ,
  • Diego Serrano ,
  • Shukai Wang
Minjie Chen
Princeton University

Corresponding Author:[email protected]

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Haoran Li
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Diego Serrano
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Shukai Wang
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This paper presents the MagNet-AI platform as an online platform to demonstrate the “Neural Network as Datasheet” concept for B–H loop modeling and material recommendation of power magnetics across wide operation range. Instead of directly presenting the measured characteristics of magnetic core materials as time sequences, we employ a neural network to capture the B–H loop mapping relationships of magnetic materials under different excitation waveforms at different temperatures and dc-bias. Both LSTM and Transformer based neural network models are developed, verified, and compared. The training and inference process of the neural network are fully automated to minimize the impact of human error. The neural network can be used to rapidly predict B–H loops under different operating conditions, compare materials, and recommend materials for design. Neural networks are also effective in compressing the information contained in the raw database, and enable rapid material evaluation and comparison.