Numerical methods for uncertainty quantification [MAT08, 2 ECTS]

The aim of this course is to provide an introduction to uncertainty quantification for differential equations 
with random coefficients. We will focus on the forward problem of assessing the propagation of the 
uncertainty in the input coefficients to the solution of the equation and discuss some standard and 
state-of-the-art computational methods, such as the Monte Carlo method, sparse grids, and the polynomial chaos
expansion. Moreover, we will address multi-fidelity techniques, such as the multi-level Monte Carlo method and  
multi-index stochastic collocation, that leverage low and high-fidelity model solutions to achieve the best 
trade-off between accuracy and computational cost. To complement the discussion, we will present some real-world applications.

Possible timetable:

April 03 13:30-15:30 room I

April 05 11:30-13:30 room M

April 09 8:30-10:30 room I

April 10 13:30-15:30 room atrio CV1

April 11 13:30-15:30 room G

April 12 11:30-13:30 room M

Zoom meeting link: https://univr.zoom.us/j/97004181713 (password given by the first six significant digits of acos(-1)).