About Saugat Upreti

I am a PhD researcher in Mechanical Engineering at Cleveland State University. My research sits at the intersection of physics-based modeling and data-driven machine learning, with a focus on understanding and predicting the behavior of complex physical systems.

My current work applies scientific machine learning to the dynamics of von Willebrand Factor (vWF) polymer chains under shear flow — exploring how latent representations from simulation data can capture the coil-to-stretch transition and predict flow-induced conformational changes.

I develop scalable computational workflows that bridge classical polymer physics with modern learning architectures, emphasizing interpretability and reproducibility throughout.