Parameter identification in a probabilistic setting BV Rosić, A Kučerová, J Sýkora, O Pajonk, A Litvinenko, HG Matthies Engineering Structures 50, 179-196, 2013 | 119 | 2013 |
Application of hierarchical matrices for computing the Karhunen–Loeve expansion BN Khoromskij, A Litvinenko, HG Matthies Computing 84 (1), 49-67, 2009 | 118 | 2009 |
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 | 91 | 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 | 84 | 2015 |
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 | 84 | 2012 |
Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats M Espig, W Hackbusch, A Litvinenko, HG Matthies, P Wähnert Computers & Mathematics with Applications 67 (4), 818-829, 2014 | 83 | 2014 |
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 | 78 | 2019 |
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, 2013 | 77 | 2013 |
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 | 75* | 2016 |
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 | 66 | 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, 1-21, 2016 | 61 | 2016 |
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 | 55 | 2013 |
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 | 46 | 2017 |
Parametric and uncertainty computations with tensor product representations HG Matthies, A Litvinenko, O Pajonk, BV Rosić, E Zander Uncertainty Quantification in Scientific Computing: 10th IFIP WG 2.5 Working …, 2012 | 45 | 2012 |
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 | 42 | 2014 |
Methods for statistical data analysis with decision trees V Berikov, A Litvinenko Novosibirsk, Sobolev Institute of Mathematics, 2003 | 42 | 2003 |
HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification A Litvinenko, R Kriemann, MG Genton, Y Sun, DE Keyes MethodsX 7, 100600, 2020 | 30 | 2020 |
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 | 28 | 2013 |
Direct Bayesian update of polynomial chaos representations BV Rosic, A Litvinenko, O Pajonk, HG Matthies Journal of Computational Physics, 2011 | 28 | 2011 |
Inverse problems and uncertainty quantification A Litvinenko, HG Matthies arXiv preprint arXiv:1312.5048, 2013 | 26 | 2013 |