1 citations · 1 across the 3 of their papers we have counts for
3 papers · 1 filter
On the Numerical Approximation of the Karhunen-Loève Expansion for Random Fields with Random Discrete Data
Michael Griebel, Guanglian Li, Christian Rieger
In many applications, random fields reflect uncertain parameters, and often their moments are part of the modeling process and thus well known. However, there are practical situati…
A fault-tolerant domain decomposition method based on space-filling curves
Michael Griebel, Marc-Alexander Schweitzer, Lukas Troska
We propose a simple domain decomposition method for -dimensional elliptic PDEs which involves an overlapping decomposition into local subdomain problems and a global coarse prob…
On the Numerical Approximation of the Karhunen-Loève Expansion for Lognormal Random Fields
Michael Griebel, Guanglian Li
The Karhunen-Loève (KL) expansion is a popular method for approximating random fields by transforming an infinite-dimensional stochastic domain into a finite-dimensional parameter…