From the 1 of 7 linked papers with an AI index.
7 papers
Generation of representative powder particle packing in 2D/3D: which tool for which application?
Antoine Tainturier, Louis Lemarquis, Victor Szczepan +1
The paper benchmarks four open‑source tools for generating dense 2D/3D powder particle packings, comparing their ability to match target size distributions and packing density whil…
Physics-Informed Attention Mechanism and Generalization Capability of Deep Learning-Based Grain Growth Evolution Prediction
Pungponhavoan Tep, Marc Bernacki
Machine Learning (ML) models for grain growth prediction are typically trained on idealized synthetic data, yet practical applications require generalization to conditions outside…
Predicting Grain Growth Evolution Under Complex Thermal Profiles with Deep Learning through Thermal Descriptor Modulation
Pungponhavoan Tep, Marc Bernacki
Predicting microstructure evolution during thermomechanical treatment is essential for determining the final mechanical properties of a material, yet conventional simulations based…
High-fidelity level-set modeling of polycrystalline grain growth
Tianchi Li, Marc Bernacki
Accurate modeling of polycrystalline microstructure evolution under strong crystallographic heterogeneities remains a major challenge for full-field numerical methods at the mesosc…
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…
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…