This document may be downloaded, read, stored and printed for your own use within the limits of § 53 UrhG but it may not be distributed via the internet or passed on to external parties.Dieses Dokument darf im Rahmen von § 53 UrhG zum eigenen Gebrauch kostenfrei heruntergeladen, gelesen, gespeichert und ausgedruckt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.Eigel, MartinNeumann, JohannesSchneider, ReinholdWolf, Sebastian2017-11-142019-06-2820172198-5855https://doi.org/10.34657/2719https://oa.tib.eu/renate/handle/123456789/2419This paper examines a completely non-intrusive, sample-based method for the computation of functional low-rank solutions of high dimensional parametric random PDEs which have become an area of intensive research in Uncertainty Quantification (UQ). In order to obtain a generalized polynomial chaos representation of the approximate stochastic solution, a novel black-box rank-adapted tensor reconstruction procedure is proposed. The performance of the described approach is illustrated with several numerical examples and compared to Monte Carlo sampling.application/pdfeng510Non-intrusivetensor reconstructionpartial differential equations with random coefficientstensor representationtensor trainuncertainty quantificationlow-rankReport