MSEE Short Course on Uncertainty Quantification in Physics-based Modeling using Python

This in-person three-day short course will introduce participants to the practice of uncertainty quantification (UQ) in computational modeling of physical systems using the Python programming language. Participants will be introduced to a variety of UQ tasks including uncertainty propagation, surrogate modeling, and Bayesian inference using the UQpy toolbox. By the end of the course, it is the goal that students will have the knowledge to begin applying UQ to computational models in their respective fields of study.

In particular, attendees will learn how to:

• Link a model to the UQpy software to execute UQ tasks.
• Propagate uncertainties through a computational model using simulation-based methods in UQpy
• Construct a surrogate model using polynomial chaos expansions and Gaussian process regression
• Infer the parameters of a model using Bayesian inference

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