Uncertainty Quantification, Sensitivity Analysis, and Machine Learning

High-to-Low Multi-Physics Modeling and Uncertainty Propagation Using NEAMS Tools

10:00–10:20AM MST

Joseph Fustero (NCSU), Walker Powell (NCSU), Kan Ni (NCSU), Nilay Atul Kulkarni (NCSU), Gregory K. Delipei (NCSU), David Holler (NCSU), Jason Hou (NCSU), Marco Delchini (ORNL), Ralph Smith (NCSU), Maria Avramova (NCSU)

Why is Uncertainty Quantification Important for Machine Learning Models?

10:20–10:40AM MST

Xu Wu (NCSU), Gregory Delipei (NCSU), Maria Avramova (NCSU), Kostadin Ivanov (NCSU)

Multifidelity Uncertainty Quantification in Stochastic Media Transport Problems

10:40–11:00AM MST

Gianluca Geraci (Sandia), Aaron J. Olson (Sandia)

A Sampling-Based Approach to Solve Sobol' Indices Using Variance Deconvolution for Arbitrary Uncertainty Distributions

11:00–11:20AM MST

Aaron J. Olson (Sandia), Kayla B. Clements (Oregon State), James M. Petticrew (Univ. Sheffield)