3.00 Credits
Introduces classic and recent methodological developments in experimental design, statistical modeling, and uncertainty quantification of computer experiments. Response surface analysis, Gaussian process modeling, Bayesian optimization, space-filling designs, sensitivity analysis, and statistical calibration are covered. Students are expected to have completed a graduate-level course in linear regression before enrolling in this course.