Parameterization of the Stoner-Wohlfarth model of magnetic hysteresis

TL;DR

This paper introduces a machine learning-based parametrization of the Stoner-Wohlfarth model, enabling faster and easier computation of magnetic hysteresis for practical applications.

Contribution

It presents a novel machine learning approach to approximate the Stoner-Wohlfarth model, significantly reducing computation time for magnetic hysteresis analysis.

Findings

Achieved faster computation of the model

Demonstrated effective fitting to experimental data

Provided a practical tool for magnetic hysteresis analysis

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.