Inverse estimation of parameters for the magnetic domain via dynamics matching using visual-perceptive similarity

Abstract

The estimation of parameters for the magnetic domain (i.e. magnetic domain parameters) based on their time evolution patterns created by magnetic spins is necessary in the development of magnetic materials. In this study, we develop a method for the inverse estimation of magnetic domain parameters that produce simulation results like those of a set of magnetic domain patterns given the time evolution patterns of magnetic domains. Further, we utilize an experimental method based on Gaussian process regression for the efficient search of magnetic domain parameters. We adopted a design of experiments based on Gaussian process regression for efficiently searching magnetic domain parameters. Portilla–Simoncelli statistics is a high-dimensional texture feature based on human visual perception, and it can quantitatively evaluate patterns in texture structure. We show that the proposed method can simultaneously and accurately inverse estimate three magnetic domain parameters.

Keywords

• magnetic material
• magnetic domain pattern
• gaussian process regression
• texture features
• time dependent ginzburg–landau equation