Zurück zur Übersicht Donnerstag, 20.09.2018
Donnerstag | 17.00 Uhr | Low-Rank Tensor Decompositions for High-Dimensional Uncertainty Quantification: Dr. Sergey Dolgov, University of Bath, Department of Mathematical Sciences Uncertainty quantification and inverse problems in many variables are pressingly needed tasks, yet high-dimensional functions are notoriously difficult to integrate in order to compute desired quantities of interest. Functional approximations, in particular the low-rank separation of variables into tensor product decompositions, have become popular for reducing the computational cost of high-dimensional integration down to linear scaling in the number of variables. However, tensor approximations may be inefficient for non-smooth functions. Sampling based Monte Carlo methods are more general, but they may exhibit a very slow convergence, overlooking a hidden structure of the function. In this talk we review tensor product approximations for the problem of uncertainty quantification and Bayesian inference. This allows efficient integration of smooth PDE solutions and quantities of interest. Moreover, we can use the low-rank approximation to construct efficient proposal density in the MCMC algorithm for inverse problems. This combined MCMC method is more accurate also if the quantity of interest is not smooth, such as the indicator function of an event. Adresse Mathematikon Seminar Room A / Ground Floor Im Neuenheimer Feld 205 69120 Heidelberg Homepage Veranstaltung www.iwr.uni-heidelberg.de/events Veranstalter Interdisciplinary Center for Scientific Computing (IWR) Homepage Veranstalter Kontakt |