Inferring Magnetic Material Parameters from Statistical Measures in Strongly Fluctuating Magnetization Dynamics
Magnetic material parameters such as the exchange stiffness and magnetic anisotropy govern the behavior and functionality of magnetic systems, yet their local inference from magnetization data remains challenging, particularly in strongly fluctuating regimes with polycrystalline or multiphase microstructure, where conventional texture-based methods become unreliable. We introduce a magnetization-only framework for inferring material parameters from thermally driven magnetization dynamics. Using micromagnetic simulations, we extract statistical quantities such as temporal mean and latent entropy from the magnetization dynamics, fit models to these descriptors, and invert the models to infer material parameters. We show that this framework enables material-parameter inference as well as grain-boundary detection in a heterogeneous sample. Among the descriptors considered, latent entropy yields more accurate parameter estimates than the temporal mean. Our results establish latent entropy as an efficient descriptor for inferring magnetic material parameters from dynamical magnetization data and point toward its use for experimental parameter extraction at high temperatures and, more broadly, under strongly fluctuating conditions.