Scientific Machine Learning

Our group is dedicated to the development of advanced scientific machine learning algorithms tailored for predicting material behaviors and properties, catering to a wide range of engineering applications.

Rapid prediction of grain boundary network evolution in nanomaterials utilizing a generative machine learning approach

Integrating uncertainty into deep learning models for enhanced prediction of nanocomposite materials’ mechanical properties