12 citations · 29 across the 6 of their papers we have counts for
6 papers
Adaptive non-intrusive reconstruction of solutions to high-dimensional parametric PDEs
Martin Eigel, Nando Farchmin, Sebastian Heidenreich +1
Numerical methods for random parametric PDEs can greatly benefit from adaptive refinement schemes, in particular when functional approximations are computed as in stochastic Galerk…
Efficient approximation of high-dimensional exponentials by tensornetworks
Martin Eigel, Nando Farchmin, Sebastian Heidenreich +1
In this work a general approach to compute a compressed representation of the exponential of a high-dimensional function is presented. Such exponential functions play…
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence
Anna Andrle, Nando Farchmin, Paul Hagemann +3
Grazing incidence X-ray fluorescence is a non-destructive technique for analyzing the geometry and compositional parameters of nanostructures appearing e.g. in computer chips. In t…
Efficient Bayesian inversion for shape reconstruction of lithography masks
Nando Farchmin, Martin Hammerschmidt, Philipp-Immanuel Schneider +4
Background: Scatterometry is a fast, indirect and non-destructive optical method for quality control in the production of lithography masks. To solve the inverse problem in complia…
An efficient approach to global sensitivity analysis and parameter estimation for line gratings
Nando Farchmin, Martin Hammerschmidt, Philipp-Immanuel Schneider +4
Scatterometry is a fast, indirect and nondestructive optical method for the quality control in the production of lithography masks. Geometry parameters of line gratings are obtaine…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…