Math 7390 - Topics in Numerical Analysis

Instructor:Xiaoliang Wan
Lecture:MWF 1:30-2:20pm, 240 Lockett Hall
Office Hours:MW 3:00-4:30pm
Description:This is an introductory course on uncertainty quantification (UQ). Uncertainty quantification is interested in how to deal with uncertainty in realistic applications, which has received much attention in both academia and industry. The main mathematical model will be partial differential equations subject to uncertainty, where the source of uncertainty includes physical coefficients, initial/boundary conditions, forcing, etc. In this course, we will introduce numerical strategies to deal with typical problems in uncertainty quantification. Topics include approximation of random field, polynomial chaos expansion, multi-level Monte Carlo method, Bayesian inverse problem and numerical large deviation principle.