Machine Learning Model for High-Frequency Magnetic Loss Predictions Based on Loss Map by a Measurement Kit

Accurately forecasting the losses in high-frequency magnetic materials is a significant challenge when optimizing the design of high-frequency (HF) magnetic components. Existing models do not adequately consider the intricate interactions among geometry, and temperature factors, which have distinct and substantial impacts on core losses. A new method is introduced, which utilizes a Deep Neural Network (DNN) model to construct parameterized models for high-frequency magnetic core loss based on measurement data. The DNN employs the Gaussian Error Linear Unit (GELU) activation function and Huber loss function, and its performance is compared to that of a conventional Rectified Linear Unit (ReLU) activation and Mean Squared Error (MSE) loss function. The proposed DNN demonstrates significantly higher accuracy and improved robustness.