UQ for OpInf

Embedding UQ in Operator Inference Methods (2D NS Equations)

Duke UQ Group Fall 2023

Hike at Umstead State Park, NC (Fall 2023)

Riemannian Model UQ

Developing new models to account for model-form uncertainties, using Riemannian manifold description

Brain Light

Developing new stochastic tools for stochastic multiphysics simulations on patient-specific brain geometries (collaboration with Prof. L. Gomez at Purdue University)

3D printed titanium scaffold

Developing and validating a new stochastic modeling framework for uncertainty quantification on materials produced by additive manufacturing (funded by NSF)

Guilleminot Lab

Uncertainty Quantification (UQ) Group at Duke University

We develop methods, models and algorithms at the intersection of Computational Mechanics, Materials Science, (Probability Theory and Statistics) ⊃ Uncertainty Quantification, and Scientific Machine Learning. We combine physics-based constraints with mathematical representations to enhance predictive capabilities in a variety of challenging problems across a wide span of scales, ranging from atomistic simulations on next-generation batteries to continuum models for additive manufacturing, biomedical implants, and naval applications (to name a few). We have tested our approaches in collaborative projects with Sandia National Laboratories and the U.S. Naval Research Laboratory.

 

NSF logo

Financial support from the Army Research Office, Duke University, the National Science Foundation, the U.S. Naval Research Laboratory, and Sandia National Laboratories is gratefully acknowledged.