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Title: Dynamic ferromagnetic hysteresis modelling using a preisach-recurrent neural network model
Authors: Grech, Christian
Buzio, Marco
Pentella, Mariano
Sammut, Nicholas
Keywords: Machine learning
Metals -- Magnetic properties
Particle accelerators
Ferromagnetic materials
Piezoelectric devices
Issue Date: 2020
Publisher: MDPI AG
Citation: Grech, C., Buzio, M., Pentella, M., & Sammut, N. (2020). Dynamic ferromagnetic hysteresis modelling using a preisach-recurrent neural network model. Materials, 13(11), 2561.
Abstract: In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model's parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model's potential in the field of materials science.
Appears in Collections:Scholarly Works - FacICTMN

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