Field Computation in Media Exhibiting Hysteresis Using Hopfield Neural Networks

Assessment of local magnetization in objects exhibiting hysteresis is crucial to the accurate design and performance estimation for a wide range of electromagnetic devices. In the past, different efforts have been carried out to incorporate hysteresis models in field computation approaches. While those models varied in methodologies, they shared a common goal of offering an accurate and computationally efficient field computation tool. Recently, it was demonstrated that a two-dimensional (2-D) vector hysteresis operator may be realized using a tri-node Hopfield neural network (HNN). The purpose of this article is to offer a novel 2-D field computation approach in media exhibiting hysteresis that uses the aforementioned hysteresis operator. The approach is based on incorporating domain-to-domain interactions in the overall network energy formulation while using typical HNN energy minimization algorithms. Details of the proposed model, numerical simulations, and comparisons with finite-element calculations are given in the article.