Alexander Litvinenko

Orcid: 0000-0001-5427-3598

According to our database1, Alexander Litvinenko authored at least 24 papers between 2003 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Uncertainty quantification in the Henry problem using the multilevel Monte Carlo method.
CoRR, 2024

2023
Computing f -divergences and distances of high-dimensional probability density functions.
Numer. Linear Algebra Appl., May, 2023

Uncertainty quantification in coastal aquifers using the multilevel Monte Carlo method.
CoRR, 2023

2021
Computing f-Divergences and Distances of High-Dimensional Probability Density Functions - Low-Rank Tensor Approximations.
CoRR, 2021

Identification of unknown parameters and prediction with hierarchical matrices.
CoRR, 2021

Solving weakly supervised regression problem using low-rank manifold regularization.
CoRR, 2021

Weakly Supervised Regression Using Manifold Regularization and Low-Rank Matrix Representation.
Proceedings of the Mathematical Optimization Theory and Operations Research, 2021

On a Weakly Supervised Classification Problem.
Proceedings of the Analysis of Images, Social Networks and Texts, 2021

2020
Iterative algorithms for the post-processing of high-dimensional data.
J. Comput. Phys., 2020

2019
Likelihood approximation with hierarchical matrices for large spatial datasets.
Comput. Stat. Data Anal., 2019

Post-Processing of High-Dimensional Data.
CoRR, 2019

Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability.
CoRR, 2019

Semi-Supervised Regression using Cluster Ensemble and Low-Rank Co-Association Matrix Decomposition under Uncertainties.
CoRR, 2019

Tucker Tensor Analysis of Matérn Functions in Spatial Statistics.
Comput. Methods Appl. Math., 2019

2017
Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties - Comparison of Approaches.
SIAM/ASA J. Uncertain. Quantification, 2017

2016
Parameter estimation via conditional expectation: a Bayesian inversion.
Adv. Model. Simul. Eng. Sci., 2016

2015
Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format.
SIAM/ASA J. Uncertain. Quantification, 2015

2014
To Be or Not to Be Intrusive? The Solution of Parametric and Stochastic Equations - the "Plain Vanilla" Galerkin Case.
SIAM J. Sci. Comput., 2014

Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats.
Comput. Math. Appl., 2014

2012
Sampling-free linear Bayesian update of polynomial chaos representations.
J. Comput. Phys., 2012

Parameter Identification in a Probabilistic Setting
CoRR, 2012

2011
Parametric and Uncertainty Computations with Tensor Product Representations.
Proceedings of the Uncertainty Quantification in Scientific Computing, 2011

2009
Application of hierarchical matrices for computing the Karhunen-Loève expansion.
Computing, 2009

2003
The influence of prior knowledge on the expected performance of a classifier.
Pattern Recognit. Lett., 2003


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