Introduction and Motivation

We introduce the material under the umbrella of uncertainty quantification. We examine its context from topics such as verification & validation, aleatory vs. epistemic uncertainty, and methods for characterizing uncertainties.

We also review some basic concepts in probability and approximation theory.


Homework 0Due April 12 at the beginning of class.


demo.tgzThis is the MATLAB demo we did in the first lecture.
Lecture1.pptxSlides from the first lecture.
classnotesplato.rtfBrandon's notes from our class brainstorming exercise during the second lecture.
Probability Notes Notes we used for the 3rd lecture.
approxtheory.tgz MATLAB demo for the approximation theory from the fourth lecture. You'll need the PMPack suite of MATLAB tools.


Probability Text An online version of the text used in a former STAT116 course. Chapters 2, 5, and 6 are the most relevant for our class.
STAT116 Old course website for STAT116: Introductory Probabilty.
AA222 Course website for Intro to Multidisciplinary Design Optimization -- a good reference for reviewing optimization.
Convex Optimization Book Stephen Boyd's book on optimization -- another good reference.
Chebyshev and Fourier Spectral Methods John P. Boyd's (no relation to Stephen as far as I know) book on spectral methods. The second chapter gives an excellent intuitive introduction to the convergence of Fourier/Chebyshev series
Chebfun and Approximation Theory Chebfun is a Matlab suite for computing with functions via their Chebyshev expansions. This guide uses Chebfun to explore some basic approximation theory.
Approximation Theory and Approximation Practice Lloyd N. Trefethen's upcoming book on approximation theory using Chebfun.