CNN and hand-crafted feature networks recover magnetic field (~3.8 µT), temperature (~0.12 K), and hysteresis branch from one quantitative magneto-optical domain map of Bi:YIG.
Magnetic Hamiltonian parameter estimation using deep learning techniques,
2 Pith papers cite this work, alongside 49 external citations. Polarity classification is still indexing.
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representative citing papers
An adapted U-Net model trained on mean-field phase diagrams accurately predicts Hamiltonian parameters for a cuprate superconductor when validated on Monte Carlo simulation data.
citing papers explorer
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Decoding magnetic texture
CNN and hand-crafted feature networks recover magnetic field (~3.8 µT), temperature (~0.12 K), and hysteresis branch from one quantitative magneto-optical domain map of Bi:YIG.
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Predicting parameters of a model cuprate superconductor using machine learning
An adapted U-Net model trained on mean-field phase diagrams accurately predicts Hamiltonian parameters for a cuprate superconductor when validated on Monte Carlo simulation data.