Neural-Network-based Homogenized Model for Ferromagnetic Laminated Cores

The finite element modeling of hysteresis and eddy currents in ferromagnetic laminated cores is intricate and costly. In consequence, magnetic losses are often only evaluated a posteriori in electrical machine modeling. This leads however to potentially inaccurate results, as the effect of magnetic losses on the field computation is neglected. Deep learning offers an efficient solution to this problem. Two neural network architectures able to replicate hysteretic behaviors are discussed in this paper. Their fast and accurate inference makes them good candidates to serve as material laws in multiscale finite element simulations.