UQClass_AE598_Fall2019

Slides and Jupyter Notebook files for the invited lectures "An Introduction to Uncertainty Quantification for Predictive Science", Prof. M. Panesi's Uncertainty Quantification class (AE598), UIUC, Fall 2019

The slides are divided in Part I and II

The Jupyter Notebook files are Theano + PyMC3 implementations of three probabilistic models for a mass-spring-damper sytem, with (hyper-)parameters reconstructed via Bayesian inference (MCMC). The goal is to quantify the uncertainty affecting the prediction of design quantities of interest (QoIs) due to parameter, model, and observation errors.