Math 7390 - Topics in Numerical Analysis
Syllabus
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.
Grade:
Homework
| 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. |
| Grade: |