Scientific Machine Learning

We develop advanced scientific machine learning algorithms tailored for a wide range of applications.

Publication:

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

A machine learning framework for missing and imbalanced data in marketing analytics