The Application of Neural Networks to the Modeling of Magnetic Hysteresis

Accurately modeling magnetic hysteresis plays a crucial role in developing precise digital twins for low-frequency electromagnetic systems. However, in large 3-D analysis systems, the evaluation of hysteresis performance at hundreds of thousands of points in components containing magnetic steels is challenging. It is of utmost importance that any modeling system can assess the hysteresis performance within the shortest possible timeframe. The utilization of neural networks (NNs) offers the potential to achieve this objective. This article provides a comprehensive review of various NN architectures that can be employed to address this requirement. Two types of learning approaches are explored: completely data-driven approaches and hybrid approaches that combine data and hysteresis laws. A comparative study is conducted to analyze the predictive power and computational cost of the different architectures under investigation.