Parameterization of the Stoner-Wohlfarth model of magnetic hysteresis

Abstract

The Stoner-Wohlfarth is the most used model of magnetic hysteresis, but its computation is time-consuming. We use machine learning to approximate piecewise this model by easy-to-compute analytic functions. Our parametrization is suitable for fast quantitative evaluations and fitting experimental data, which we exemplify.