Scientific Machine Learning

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

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

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

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

A Machine Learning Framework for Preventing Cracking in Semiconductor Materials

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