# Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning

Source: [Detection of Non-uniformity in Parameters for Magnetic Domain Pattern Generation by Machine Learning](<https://www.sota2.com/research/paper/68fa459c1e209131ce919b4e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T05:21:48.207Z

Authors: Naoya Mamada, Masaichiro Mizumaki, Ichiro Akai, Toru Aonishi

Published: 2023-05-24T06:15:27.000Z

## Abstract

We estimate the spatial distribution of heterogeneous physical parameters involved in the formation of magnetic domain patterns of polycrystalline thin films by using convolutional neural networks. We propose a method to obtain a spatial map of physical parameters by estimating the parameters from patterns within a small subregion window of the full magnetic domain and subsequently shifting this window. To enhance the accuracy of parameter estimation in such subregions, we employ large-scale models utilized for natural image classification and exploit the benefits of pretraining. Using a model with high estimation accuracy on these subregions, we conduct inference on simulation data featuring spatially varying parameters and demonstrate the capability to detect such parameter variations.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.14764>)

[PDF](<https://arxiv.org/pdf/2305.14764.pdf>)