Uncertainty Quantification

3 credits · Graduate School

Computational methods for analysis and design of modern engineering systems under uncertainty. Emphasis on epistemic uncertainty due to data and models. Topics include stochastic finite elements; time-dependent reliability; Bayesian methods and networks; surrogate modeling; advanced simulation; global sensitivity analysis; model verification, validation, and calibration; and optimization under uncertainty. Applications to practical engineering systems. Prerequisite: CE 6300. SPRING. [3]