401-4645-67L  Numerics for Computational Uncertainty Quantification

SemesterAutumn Semester 2017
LecturersC. Schwab
Periodicitynon-recurring course
Language of instructionEnglish

AbstractThe course presents the mathematical foundation of various numerical methods
for the efficient quantification of uncertainty in partial differential equations.
Mathematical foundations include high dimensional polynomial approximation,
sparse grid approximations, generalized polynomial chaos expansions and their
summability properties, as well the computer implementation in model problems.
ObjectiveThe course will provide a survey of the mathematical properties and
the computational realization of the most widely used numerical methods
for uncertainy quantification in PDEs from engineering and the sciences.
In particular, Monte-Carlo, Quasi-Monte Carlo and their multilevel extensions
for PDEs, Sparse grid and Smolyak approximations, stochastic collocation
and Galerkin discretizations will be discussed.
Lecture notesThere will be typed lecture notes.
LiteratureLecture Notes.
Prerequisites / NoticeCompleted BSc MATH or equivalent.