In this contribution, the magnetic characterization of steel strips is studied using synthetic data of field-gradient transients, which have been produced via the finite integration technique. The material law is described and parameterized using the Jiles–Atherton model. The sensitivity of relevant magnetic indicators with respect to the material parameters is then analyzed using two global methods: Sobol’ indices and δ-sensitivity indices. In order to accelerate the evaluation of these quantities, a fast metamodel is built using machine learning techniques from a simulated dataset. The solution of the inverse problem based on a tailored learning framework is tested for the different proposed identifiers, and their suitability for the magnetic characterization of the material in question is finally discussed.

中文翻译：

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使用瞬态场测量对钢带进行磁性表征：从机器学习角度进行全局灵敏度分析和回归

在本文中，使用通过有限积分技术产生的场梯度瞬变的合成数据研究了钢带的磁性特征。使用 Jiles-Atherton 模型描述和参数化材料定律。然后使用两种全局方法分析相关磁性指标对材料参数的敏感性：Sobol'指数和δ-敏感性指数。为了加速对这些量的评估，使用来自模拟数据集的机器学习技术构建了快速元模型。针对不同的提议标识符测试了基于定制学习框架的反演问题的解决方案，并最终讨论了它们对所讨论材料的磁性表征的适用性。