Detailseite
Projekt
Adaptive Hierarchical Low Rank Formats of High-dimensional Tensors with Applications in PDEs with Stochastic Parameters
Antragsteller
Professor Dr. Lars Grasedyck
Fachliche Zuordnung
Mathematik
Förderung
Förderung von 2008 bis 2015
Projektkennung
Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 79152369
The aim of this research proposal is the further development of the data-sparse hierarchical tensor representations (hierarchical Tucker and TT) from the first phase of this project. Our goal is to introduce a dimension-adaptivity as well as a local separation technique. The latter one is required for the application to partial differential equations with stochastic parameters, as the deterministic space dependent variables are typically strongly linked to local parameters and thus destroy the (global) separability of deterministic and stochastic variables. Both techniques are not restricted to the aforementioned application area as they are of general interest for the understanding of hierarchical tensor formats.
DFG-Verfahren
Schwerpunktprogramme
