Predicting Grain Growth Evolution Under Complex Thermal Profiles with Deep Learning through Thermal Descriptor Modulation
This paper presents a deep learning framework enhanced with Feature-wise Linear Modulation (FiLM) that successfully predicts grain growth evolution under complex, time-varying thermal profiles with high accuracy and speed, overcoming the limitations of previous models restricted to constant-temperature conditions.