Probabilistic Methods for Uncertainty Quantification in Computational Models I


Over the last decade, improved measurement capabilities and computational resources have greatly accelerated method development for uncertainty quantification (UQ) in computational models of physical systems.

This talk will give an overview of UQ in computational models. In particular, I will focus on probabilistic methods for forward and inverse UQ studies with an emphasis on Polynomial Chaos (PC) expansions and Bayesian inference, respectively. I will introduce both classical and more advanced methods, highlighting major challenges with synthetic demonstrations and, time permitting, with physical applications, including CFD and climate models.

The talk will bias towards capabilities and methods available in UQTk, which is a lightweight C++/Python software package developed and maintained at Sandia-California (

Apr 19, 2016
Schlumberger-Doll Research Center