F. Zhou

Scalable autoregressive deep surrogates for complex microstructure dynamics

A machine-learning framework that employs autoregressive deep surrogates, trained on short trajectories from quantitative phase-field simulations of alloy solidification within limited spatial domains, is introduced, confirming its capability to predict complex microstructural pattern formation.