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Senior Research Scientist

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Very often mathematical models (given by partial or ordinary differential equations) contain parameters which are uncertain. Typical examples are conductivity coefficients in groundwater flow problems and porosity. The uncertain heterogeneity in the material can affect the system behaviour dramatically. One of the mostly used techniques to model such uncertainties is random fields. To solve resulting PDE with random fields (stochastic PDE) numerically one has to discretise the deterministic operator as well as the high-dimensional stochastic operator. There are different methods to discretise and to solve these stochastic PDEs: stochastic Galerkin, collocation, sparse grids, (quasi) MC etc.

To reduce the computational complexity the stochastic forward problem is approximated in a low-rank/sparse tensor data format.

W. Nowak, A. Litvinenko, Kriging accelerated by orders of magnitude: combining lowrank covariance approximations with FFT-techniques, Mathematical Geosciences, 2013, Vol. 45, Issue 4, pp 411-435

M. Espig, W. Hackbusch, A. Litvinenko, H. G. Matthies, Ph. Waehnert, Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats, Computers & Mathematics with Applications, (2012)

B. Rosic, A. Litvinenko, O. Pajonk and H. G. Matthies, Sampling-free linear Bayesian update of polynomial chaos representations, J. Comp. Physics, 231(2012), pp 5761-5787

A. Litvinenko and H. G. Matthies, Numerical Methods for Uncertainty Quantification and Bayesian update in Aerodynamics, chapter in the book Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics, pp. 267-283, Editors: B. Eisfeld, H. Barnewitz, W. Fritz, F. Thiele, Springer, 2013

M. Espig, W. Hackbusch, A. Litvinenko, H. G. Matthies and E. Zander, Efficien Analysis of High Dimensional Data in Tensor Formats, Springer LNCSE ''Sparse Grids and Applications'', vol. 88, pp 31-56, (2012)

2002-2006, Promotion in the group of Scientific computing at Max-Planck-Institut fuer Mathematik in den Naturwissenschaften, Leipzig, Germany, www.mis.mpg.de

2000-2002, Master degree in Mathematics at Novosibirsk State University and Laboratory of Data Analysis of Sobolev Institute of Mathematics www.math.nsc.ru (Russian Academy of Sciences)

1996-2000, Bachelor degree in Mathematics at Novosibirsk State University www.nsu.ru

- Computer, Electrical and Mathematical Sciences & Engineering Division.
- Member of KAUST ECRC and SRI Center for Uncertainty Quantification in Computational Science and Engineering: http://sri-uq.kaust.edu.sa/Pages/People.aspx