Scientific Machine Learning

We develop scientific machine learning algorithms that integrate data-driven approaches with physics-based modeling to accelerate discovery, enhance prediction accuracy, and enable efficient solutions across diverse scientific and engineering domains.

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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