Course Detail
Units:
3.0
Course Components:
Lecture
Description
A course on the formal process of developing science-based predictive models with quantified uncertainty. Topics covered include: identification of experimental, numerical and model parameter uncertainties; model form uncertainty, quantitative validation, linear and semidefinite programming for uncertainty quantification, Bayesian parameter estimation and uncertainty analysis, and surrogate modeling.