401-4645-67L  Numerics for Computational Uncertainty Quantification

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



Courses

NumberTitleHoursLecturers
401-4645-67 VNumerics for Computational Uncertainty Quantification3 hrs
Mon13:15-15:00HG G 26.5 »
Wed09:15-10:00HG G 26.5 »
C. Schwab
401-4645-67 UNumerics for Computational Uncertainty Quantification2 hrs
Thu08:15-10:00HG E 33.3 »
21.09.08:15-10:00HG D 7.1 »
C. Schwab

Catalogue data

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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits10 credits
ExaminersC. Schwab
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 30 minutes
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main linkMain webpage
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Groups

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Restrictions

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Offered in

ProgrammeSectionType
Doctoral Department of MathematicsGraduate SchoolWInformation
Mathematics MasterSelection: Numerical AnalysisWInformation