Speaker: Cosmin Safta (Sandia National Laboratories)
Title: Uncertainty Quantification and Machine Learning Algorithms for Physical Models - Tackling Computational Expense and High-Dimensionality
Description: This presentation will focus on analysis workflows for quantifying uncertainty in physical systems. In this context I will describe challenges posed by the computational cost and high-dimensionality associated with applications of interest to DOE (Earth System Model, Atmospheric Transport Models) and DoD (Scramjet Engine). I will outline algorithmic developments adapted to each application including both supervised and unsupervised sparse learning techniques.