Blind Efficient Method for Optimizing Jiles-Atherton Model Parameters

Abstract

We introduce a novel blind optimization method for determining the parameters of the Jiles-Atherton model of hysteresis, eliminating the need for user-provided initial guesses or search spaces. A carefully designed initialization procedure combined with a standard optimizer yields a high-performing, practical method for parameter estimation. Validation against a theoretical benchmark recovers ground-truth parameters in under half a minute with negligible error (relative error < 4×10⁻⁸). When applied to the TEAM32 electrical steel experimental benchmark, our method achieved superior accuracy than previously reported fittings, also converging in under half a minute. Consistently robust performance is further demonstrated across diverse systems, including soft ferrites, nanocrystalline alloys, and magnetostrictive compounds. The presented blind approach offers new insights into magnetic material characterization and is deployed as an automated tool for hysteresis analysis. It advances both fundamental understanding and practical applications by demonstrating the Jiles-Atherton model’s capability to describe anisotropic materials and by revealing its inherent limitations.