Geometric Analysis of Magnetic Labyrinthine Stripe Evolution via Deep Learning Segmentation
Researchers have developed a deep learning model, specifically a U-Net architecture, to analyze complex magnetic stripe patterns in bismuth-doped yttrium iron garnet films. This model is capable of robustly segmenting experimental images, even with noise and occlusions, by being trained on synthetic data. Following segmentation, a geometric analysis pipeline quantifies stripe evolution through measurements of length and curvature, revealing two distinct evolution modes related to magnetic field polarity. AI
IMPACT Provides a new tool for analyzing complex physical systems using deep learning segmentation and geometric analysis.