3.0

credits

Average Course Rating

The course introduces the theory and practice of uncertainty quantification. Methods for quantifying aleatory and epistemic uncertainty are considered, probabilistic and non-probabilistic approaches are discussed. The course introduces: propagation of uncertainty including statistical sampling methods, surrogate modeling, and numerical methods; inverse uncertainty quantification using Bayesian methods; global sensitivity analysis; and reliability/probability of failure analysis. The course is project-based and will require prior knowledge of both probability theory and coding (preferably in Python).