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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Phase-field model with double-well energy and spectral solver simulates sea-ice tensile and shear fractures, validated on benchmarks and reproducing Griffith theory crack-speed scaling.
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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A phase-field modeling approach to sea-ice fracturing
Phase-field model with double-well energy and spectral solver simulates sea-ice tensile and shear fractures, validated on benchmarks and reproducing Griffith theory crack-speed scaling.