AI 中文总结
该研究提出仅依赖磁化的框架，通过微磁模拟提取潜熵等统计量反演模型，可推断磁性材料参数及检测异质样品晶界，且潜熵比时间均值的参数估计更准确，适用于高温等强波动条件。
AI 中文摘要
交换刚度和磁各向异性等磁性材料参数决定了磁性系统的行为与功能，但从磁化数据中局部推断这些参数颇具挑战性，尤其在具有多晶或多相微结构的强波动 regime 中，传统基于织构的方法变得不可靠。我们提出一种仅依赖磁化的框架，用于从热驱动的磁化动力学中推断材料参数。通过微磁模拟，我们从磁化动力学中提取时间均值、潜熵等统计量，将模型拟合至这些描述符并反演模型以推断材料参数。我们表明该框架可实现材料参数推断及异质样品中的晶界检测。在所有考虑的描述符中，潜熵比时间均值能给出更准确的参数估计。我们的结果确立了潜熵作为从动态磁化数据推断磁性材料参数的高效描述符，并指出其可用于高温下的实验参数提取，更广泛地适用于强波动条件。
英文摘要
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.
Comments14 pages, 10 figures, 4 tables
