4 papers
Data-intrinsic approximation in metric spaces
Jürgen Dölz, Michael Multerer
Analysis and processing of data is a vital part of our modern society and requires vast amounts of computational resources. To reduce the computational burden, compressing and appr…
Quantifying uncertainty in spectral clusterings: expectations for perturbed and incomplete data
Jürgen Dölz, Jolanda Weygandt
Spectral clustering is a popular unsupervised learning technique which is able to partition unlabelled data into disjoint clusters of distinct shapes. However, the data under consi…
Local sensitivity analysis for Bayesian inverse problems
Jürgen Dölz, David Ebert
We present an extension of local sensitivity analysis, also referred to as the perturbation approach for uncertainty quantification, to Bayesian inverse problems. More precisely, w…
Fully discrete analysis of the Galerkin POD neural network approximation with application to 3D acoustic wave scattering
Jürgen Dölz, Fernando Henríquez
In this work, we consider the approximation of parametric maps using the so-called Galerkin POD-NN method. This technique combines the computation of a reduced basis via proper ort…