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Response Surface in low-rank Tensor Train Format for Uncertainty Quantification By Dr. Alexander Litvinenko (KAUST)

  • Class schedule:  Monday, May 5th, 2014 from 04:00 pm to 05:00 pm
  • Location: Building 9, Lecture Hall II, Room 2325 - SHAXC-2 Workshop
  • Seminar material available at: link
Joint work with Sergey Dolgov and  Boris Khoromsij from Max-Planck-Institut fuer Mathematik in den Naturwissenschaften, Leipzig and Hermann G. Matthies from Technische Universitaet Braunschweig, Germany.

Alexander Litvinenko joined the Stochastic Numerics Group and Strategic Initiative in Uncertainty Quantification at KAUST in 2013.  He specializes in efficient numerical methods for stochastic PDEs, uncertainty quantification, and multi-linear algebra.  He is involved in Bayesian update methods for solving inverse problems, with the goal of reducing the complexity both the stochastic forward problem as well as the Bayesian update by a low-rank (sparse) tensor data approximation. A further goal is optimal design of experiments to maximize the information gain for a given amount of experimental effort. Applications include aerodynamics, subsurface flow, and climate prediction. Alexander earned B.S. (2000) and M.S. (2002) degrees in mathematics at Novosibirsk State University, and his PhD (2006) in the group of Prof. Hackbusch at Max-Planck-Institut in Leipzig, on the combination of domain decomposition methods and hierarchical matrices for solving elliptic PDEs with jumping and oscillatory coefficients. From 2007-2013 he was a Postdoctoral Research Fellow at the TU Braunschweig in Germany where he became interested in his current research focus area of low-rank/sparse methods for uncertainty quantification as well as Bayesian updating for multi-parametric problems.​