3 citations · 6 across the 17 of their papers we have counts for
4 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
Many physical and mathematical models involve random fields in their input data. Examples are ordinary differential equations, partial differential equations and integro--different…
A dimension-oblivious domain decomposition method based on space-filling curves
Michael Griebel, Marc Alexander Schweitzer, Lukas Troska
In this paper we present an algebraic dimension-oblivious two-level domain decomposition solver for discretizations of elliptic partial differential equations. The proposed paralle…
Deep Neural Networks and PIDE discretizations
Bastian Bohn, Michael Griebel, Dinesh Kannan
In this paper, we propose neural networks that tackle the problems of stability and field-of-view of a Convolutional Neural Network (CNN). As an alternative to increasing the netwo…
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…