3 papers
cond-mat.mtrl-sci2025
InfinityEBSD : Metrics-Guided Infinite-Size EBSD Map Generation With Diffusion Models
Sterley Labady, Youssef Mesri, Daniel Pino Munoz +2
Materials performance is deeply linked to their microstructures, which govern key properties such as strength, durability, and fatigue resistance. EBSD is a major technique for cha…
cs.LG2025
Predicting Grain Growth in Polycrystalline Materials Using Deep Learning Time Series Models
Eliane Younes, Elie Hachem, Marc Bernacki
Grain Growth strongly influences the mechanical behavior of materials, making its prediction a key objective in microstructural engineering. In this study, several deep learning ap…
cond-mat.mtrl-sci2025
High-fidelity Grain Growth Modeling: Leveraging Deep Learning for Fast Computations
Pungponhavoan Tep, Marc Bernacki
Grain growth simulation is crucial for predicting metallic material microstructure evolution during annealing and resulting final mechanical properties, but traditional partial dif…