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

Autor:	Mamada, Naoya, Mizumaki, Masaichiro, Akai, Ichiro, Aonishi, Toru
Rok vydání:	2023
Předmět:	Condensed Matter - Materials Science Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 32 pages, 14 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.14764 Zobrazit plný text záznamu View this record from Arxiv