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Uncertainty Quantification and Quasi-Monte Carlo (Wintersemester 2022/23)


  • The first lecture will be held on Monday October 17, 12:15-14:00.
  • The first exercise session will be held on Tuesday October 25, 12:15-14:00.


Lectures Mon 12:15-14:00 A6/SR009 Dr. Vesa Kaarnioja
Exercises Tue 12:15-14:00 A3/SR115 Dr. Vesa Kaarnioja

General Information


High-dimensional numerical integration plays a central role in contemporary study of uncertainty quantification. The analysis of how uncertainties associated with material parameters or the measurement configuration propagate within mathematical models leads to challenging high-dimensional integration problems, fueling the need to develop efficient numerical methods for this task.

Modern quasi-Monte Carlo (QMC) methods are based on tailoring specially designed cubature rules for high-dimensional integration problems. By leveraging the smoothness and anisotropy of an integrand, it is possible to achieve faster-than-Monte Carlo convergence rates. QMC methods have become a popular tool for solving partial differential equations (PDEs) involving random coefficients, a central topic within the field of uncertainty quantification.

This course provides an introduction to uncertainty quantification and how QMC methods can be applied to solve problems arising within this field.

Target audience

The course is intended for mathematics students at the Master's level and above.


Multivariable calculus, linear algebra, basic probability theory, and MATLAB (or some other programming language).

Completing the course

The conditions for completing this course are successfully completing and submitting at least 60% of the course's exercises and successfully passing the course exam.


  • Please register to the course via Campus Management (CM), then you will be automatically registered in MyCampus/Whiteboard as well. Please note the deadlines indicated there. For further information and in case of any problems, please consult the Campus Management's Help for Students.
  • Non-FU students should register to the course in KVV (Whiteboard).

Lecture notes

Lecture notes will be published here after each week's lecture.

Exercise sheets

Weekly exercises will be published here after each lecture.


Dr. Vesa Kaarnioja vesa.kaarnioja@fu-berlin.de Arnimallee 6, room 212
Consulting hours: By appointment