Hybrid phase-field and attention-based deep learning model predicts microstructure evolution in ternary alloys up to 400 timesteps with generalization to new compositions.
Holm, Ryan Cohn, Nan Gao, Andrew R
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Mask-conditioned LDM generates synthetic TEM defect image-mask pairs that augment small experimental sets and produce up to 0.02 gain in harmonic-mean F1 for combined detection and classification with Mask R-CNN.
citing papers explorer
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Bridging Phase-Field Model and Deep Learning for Predicting 2D and 3D Microstructure Evolution in Ternary Alloys
Hybrid phase-field and attention-based deep learning model predicts microstructure evolution in ternary alloys up to 400 timesteps with generalization to new compositions.
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Improving Combined Detection and Classification of TEM Defects via Mask-Conditioned Latent Diffusion Augmentation
Mask-conditioned LDM generates synthetic TEM defect image-mask pairs that augment small experimental sets and produce up to 0.02 gain in harmonic-mean F1 for combined detection and classification with Mask R-CNN.