Alexander Litvinenko
Alexander Litvinenko
Chair of Mathematics for Uncertainty Quantification, RWTH Aachen
Verified email at - Homepage
Cited by
Cited by
Application of hierarchical matrices for computing the Karhunen–Loeve expansion
BN Khoromskij, A Litvinenko, HG Matthies
Computing 84 (1), 49-67, 2009
Parameter identification in a probabilistic setting
BV Rosić, A KučerovŠ, J Sżkora, O Pajonk, A Litvinenko, HG Matthies
Engineering Structures 50, 179-196, 2013
Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats
M Espig, W Hackbusch, A Litvinenko, HG Matthies, P Wšhnert
Computers & Mathematics with Applications 67 (4), 818-829, 2014
Sampling-free linear Bayesian update of polynomial chaos representations
BV Rosić, A Litvinenko, O Pajonk, HG Matthies
Journal of Computational Physics 231 (17), 5761-5787, 2012
Efficient analysis of high dimensional data in tensor formats
M Espig, W Hackbusch, A Litvinenko, HG Matthies, E Zander
Sparse grids and applications, 31-56, 2012
A deterministic filter for non-Gaussian Bayesian estimation—applications to dynamical system estimation with noisy measurements
O Pajonk, BV Rosić, A Litvinenko, HG Matthies
Physica D: Nonlinear Phenomena 241 (7), 775-788, 2012
Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format
S Dolgov, BN Khoromskij, A Litvinenko, HG Matthies
IAM/ASA J. Uncertainty Quantification 3 (1), 1109-1135, 2015
To be or not to be intrusive? The solution of parametric and stochastic equations---the “plain vanilla” Galerkin case
L Giraldi, A Litvinenko, D Liu, HG Matthies, A Nouy
SIAM Journal on Scientific Computing 36 (6), A2720-A2744, 2014
Inverse Problems in a Bayesian Setting
Hermann G. Matthies, Elmar Zander, Oliver Pajonk, Bojana V Rosić, Alexander ...
Computational Methods for Solids and Fluids Multiscale Analysis, Probability†…, 2016
Likelihood approximation with hierarchical matrices for large spatial datasets
A Litvinenko, Y Sun, MG Genton, DE Keyes
Computational Statistics & Data Analysis 137, 115-132, 2019
Kriging and spatial design accelerated by orders of magnitude: Combining low-rank covariance approximations with FFT-techniques
W Nowak, A Litvinenko
Mathematical Geosciences 45 (4), 411-435, 2013
Parametric and uncertainty computations with tensor product representations
HG Matthies, A Litvinenko, O Pajonk, BV Rosić, E Zander
IFIP Working Conference on Uncertainty Quantification, 139-150, 2011
Computation of the response surface in the tensor train data format
S Dolgov, BN Khoromskij, A Litvinenko, HG Matthies
arXiv preprint arXiv:1406.2816, 2014
Parameter estimation via conditional expectation: a Bayesian inversion
HG Matthies, E Zander, BV Rosić, A Litvinenko
Advanced modeling and simulation in engineering sciences 3 (1), 1-21, 2016
Methods for statistical data analysis with decision trees
V Berikov, A Litvinenko
Novosibirsk, Sobolev Institute of Mathematics, 2003
Quantification of airfoil geometry-induced aerodynamic uncertainties---comparison of approaches
D Liu, A Litvinenko, C Schillings, V Schulz
SIAM/ASA Journal on Uncertainty Quantification 5 (1), 334-352, 2017
Sampling and low-rank tensor approximation of the response surface
A Litvinenko, HG Matthies, TA El-Moselhy
Monte Carlo and Quasi-Monte Carlo Methods 2012, 535-551, 2013
Direct Bayesian update of polynomial chaos representations
BV Rosic, A Litvinenko, O Pajonk, HG Matthies
Journal of Computational Physics, 2011
Data Sparse Computation of the Karhunen‐LoŤve Expansion
BN Khoromskij, A Litvinenko
AIP Conference Proceedings 1048 (1), 311-314, 2008
Sparse data representation of random fields
A Litvinenko, HG Matthies
PAMM: Proceedings in Applied Mathematics and Mechanics 9 (1), 587-588, 2009
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