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Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model.

Christian Grech^1,2, Marco Buzio², Mariano Pentella^2,3

¹Faculty of Information and Communications Technology, University of Malta, MSD2080 Msida, Malta.

Materials (Basel, Switzerland)

|June 10, 2020

Summary

A new Preisach-recurrent neural network model accurately predicts dynamic hysteresis in ARMCO pure iron. This model shows strong predictive capabilities even with limited training data, crucial for particle accelerator magnets.

Keywords:

ARMCO pure iron Preisach dynamic hysteresis loop machine learning magnetic properties particle accelerators recurrent neural networks

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Area of Science:

Materials Science
Computational Physics
Machine Learning

Background:

Soft magnetic materials like ARMCO pure iron are essential for particle accelerator magnets.
Accurate prediction of dynamic hysteresis is critical for optimizing magnetic device performance.
Existing models may require extensive data or struggle with dynamic behaviors.

Purpose of the Study:

To develop a novel Preisach-recurrent neural network model for predicting dynamic hysteresis.
To validate the model's parameter identification and predictive accuracy.
To assess the model's generalization capabilities on unseen data.

Main Methods:

Coupling a recurrent neural network with Preisach play operators.
Implementing a novel validation method for parameter identification.
Training the model with a limited dataset of six hysteresis loops.

Main Results:

The model achieved a Normalised Root Mean Square Error (NRMSE) below 0.7% for predicting magnetic flux density.
Effective prediction was demonstrated using ramp-rates not included in the training set.
The model accurately described ferromagnetic dynamic hysteresis with minimal training data.

Conclusions:

The proposed Preisach-recurrent neural network model offers a powerful tool for understanding and predicting dynamic hysteresis.
This approach shows significant potential for materials science applications, particularly in accelerator magnet design.
The model's efficiency in learning from limited data makes it highly practical.