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Updated: Jun 18, 2026

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement
Published on: November 7, 2017
Disorder identification in hysteresis data: recognition analysis of the random-bond-random-field Ising model
O S Ovchinnikov1, S Jesse, P Bintacchit
1Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996, USA.
Abstract:
An approach for the direct identification of disorder type and strength in physical systems based on recognition analysis of hysteresis loop shape is developed. A large number of theoretical examples uniformly distributed in the parameter space of the system is generated and is decorrelated using principal component analysis (PCA). The PCA components are used to train a feed-forward neural network using the model parameters as targets. The trained network is used to analyze hysteresis loops for the investigated system. The approach is demonstrated using a 2D random-bond-random-field Ising model, and polarization switching in polycrystalline ferroelectric capacitors.
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