3 papers
cond-mat.mtrl-sci2026
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…
cond-mat.mtrl-sci2026
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…
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…