Uncertainty quantification: Higher order QMC Galerkin discretization for PDEs with random coefficients
Title: Uncertainty quantification: Higher order QMC Galerkin discretization for PDEs with random coefficients
Speaker : Josef Dick (
Date: 16:00-17:00pm, January 9, 2014
Place: Conference Room A304, Department of Mathematical Sciences
Abstract: We construct quasi-Monte Carlo methods to approximate the expected values of linear functionals of Galerkin discretizations of PDEs with random coefficients which depend on a possibly infinite sequence of parameters. Such problems arise in the numerical solution of differential equations with random field inputs. We analyze the regularity of the solutions with respect to the parameters in terms of the rate of decay of the fluctuations of the input field. In our analysis we use a non-standard Banach space setting and introduce ``smoothness-driven product and order dependent (SPOD)'' weights for which we show fast CBC construction.
Profile of speaker：PhD 2004 at the University of New South Wales (UNSW), supervisor Prof. Ian Sloan, Associate Professor in the School of Mathematics and Statistics at UNSW, Australian Research Council Queen Elizabeth 2 Fellow 2010-2014, Information-based complexity prize 2013, Christopher Heyde Medal of the Australian Academy of Science 2012, 70 Publications.
Contact：Xiaoqun Wang