While physics-based computational models offer strong predictive capability, their computational efficiency is often insufficient for direct industrial application. To bridge this gap, we aim to develop artificial intelligence (AI) models that can serve as effective surrogates for these physics-based models.
Computationally Augmented Data-Driven Modeling for Materials Design