A. Skarlatos, R. Miorelli, C. Reboud, Frenk van den Berg
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Magnetic characterisation of steel strips using transient field measurements: global sensitivity analysis and regression from a machine-learning perspective
In this contribution, the magnetic characterisation of steel strips is studied using synthetic data of field-gradient transients, which have been produced via the finite integration technique (FIT). The material law is described and parametrized using the Jiles-Atherton (JA) model. The sensitivity of relevant magnetic indicators with respect to the material parameters is then analyzed using two global methods: Sobol indices and $\delta$-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 characterisation of the material in question is finally discussed.
期刊介绍:
An interdisciplinary journal combining mathematical and experimental papers on inverse problems with theoretical, numerical and practical approaches to their solution.
As well as applied mathematicians, physical scientists and engineers, the readership includes those working in geophysics, radar, optics, biology, acoustics, communication theory, signal processing and imaging, among others.
The emphasis is on publishing original contributions to methods of solving mathematical, physical and applied problems. To be publishable in this journal, papers must meet the highest standards of scientific quality, contain significant and original new science and should present substantial advancement in the field. Due to the broad scope of the journal, we require that authors provide sufficient introductory material to appeal to the wide readership and that articles which are not explicitly applied include a discussion of possible applications.