1 citations · 1 across the 2 of their papers we have counts for
5 papers
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…
Sparse tensor product approximation for a class of generalized method of moments estimators
Alexandros Gilch, Michael Griebel, Jens Oettershagen
Generalized Method of Moments (GMM) estimators in their various forms, including the popular Maximum Likelihood (ML) estimator, are frequently applied for the evaluation of complex…
Maximum Approximated Likelihood Estimation
Michael Griebel, Florian Heiss, Jens Oettershagen +1
Empirical economic research frequently applies maximum likelihood estimation in cases where the likelihood function is analytically intractable. Most of the theoretical literature…
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…