S. Kucherenko

发表

A new derivative based importance criterion for groups of variables and its link with the global sensitivity indices

I. Sobol, S. Kucherenko, 2010, Comput. Phys. Commun..

Derivative‐Based Global Sensitivity Analysis: Upper Bounding of Sensitivities in Seismic‐Hazard Assessment Using Automatic Differentiation

A. Griewank, F. Scherbaum, F. Cotton, 2017 .

Global sensitivity indices for nonlinear mathematical models. Review

I. Sobol, S. Kucherenko, 2005 .

Derivative based global sensitivity measures

B. Iooss, S. Kucherenko, 2014, 1412.2619.

Application of Quasi Monte Carlo and Global Sensitivity Analysis to Option Pricing and Greeks

S. Kucherenko, M. Bianchetti, Stefano Scoleri, 2017 .

Pricing and Risk Management with High-Dimensional Quasi Monte Carlo and Global Sensitivity Analysis

Marco Bianchetti, Sergei Kucherenko, S. Kucherenko, 2015, 1504.02896.

COMPARISON OF DIFFERENT NUMERICAL ESTIMATORS FOR MAIN EFFECT GLOBAL SENSITIVITY INDICES

Shufang Song, Sergei Kucherenko, S. Kucherenko, 2015 .

Global sensitivity analysis and meta-modeling of an ethanol production process

Nilay Shah, Cleo Kontoravdi, David J. Leak, 2014 .

Comparison of Monte Carlo and Quasi Monte Carlo Sampling Methods in High Dimensional Model Representation

Nilay Shah, Balazs Feil, Sergei Kucherenko, 2009, 2009 First International Conference on Advances in System Simulation.

Global Sensitivity Analysis Challenges in Biological Systems Modeling

Efstratios N. Pistikopoulos, Athanasios Mantalaris, Alexandros Kiparissides, 2009 .

Exploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques

A. Saltelli, S. Kucherenko, Daniel Albrecht, 2015, 1505.02350.

Construction and Comparison of High-Dimensional Sobol' Generators

I. Sobol, S. Kucherenko, D. Asotsky, 2011 .

Application of Global Sensitivity Indices for Measuring the Effectiveness of Quasi-Monte Carlo Methods

S. Kucherenko, Wolfgang Mauntz, 2005 .

Constrained Global Sensitivity Analysis: Sobol’ indices for problems in non-rectangular domains

N. Shah, S. Kucherenko, O. Klymenko, 2017 .

Quantile based global sensitivity measures

Lu Wang, S. Kucherenko, Shufang Song, 2016, Reliab. Eng. Syst. Saf..

Quasi-Monte Carlo Methods for Calculating Derivatives Sensitivities on the GPU

S. Kucherenko, Paul Bilokon, Casey Williams, 2022, SSRN Electronic Journal.

The importance of being scrambled: supercharged Quasi Monte Carlo

S. Kucherenko, J. Hok, 2022, 2210.16548.

Elastic-strain mechanisms for the influence of temperature, magnetic field, and pressure on the resistive and magnetic properties of magnetic semiconductors

S. Kucherenko, P. Polyakov, 2002 .

Regularities in temperature, magnetic field and pressure effect on the resistive properties of magnetic semiconductors

S. Kucherenko, P. Polyakov, 2002 .

Estimation of global sensitivity indices for models with dependent variables

Paola Annoni, Stefano Tarantola, Sergei S. Kucherenko, 2012, Comput. Phys. Commun..

Optimal experimental design based on global sensitivity analysis

Nilay Shah, Maria Rodriguez-Fernandez, Sergei Kucherenko, 2007 .

Derivative based global sensitivity measures and their link with global sensitivity indices

Sergei S. Kucherenko, Ilya M. Sobol, I. Sobol, 2009, Math. Comput. Simul..

The identification of model effective dimensions using global sensitivity analysis

Nilay Shah, Balazs Feil, Sergei S. Kucherenko, 2011, Reliab. Eng. Syst. Saf..

Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling

Nilay Shah, Frank Lemke, Sergei S. Kucherenko, 2016, Math. Comput. Simul..

Estimating the approximation error when fixing unessential factors in global sensitivity analysis

Stefano Tarantola, Sergei S. Kucherenko, Ilya M. Sobol, 2007, Reliab. Eng. Syst. Saf..

Quantile sensitivity measures based on subset simulation importance sampling

Sergei S. Kucherenko, Shufang Song, Zhiwei Bai, 2021, Reliab. Eng. Syst. Saf..

Application of Quasi Monte Carlo Methods in Global Optimization

Sergei Kucherenko, S. Kucherenko, 2006 .

Application of Deterministic Low-Discrepancy Sequences in Global Optimization

Sergei S. Kucherenko, Yury Sytsko, S. Kucherenko, 2005, Comput. Optim. Appl..

SobolHDMR: a general-purpose modeling software.

Sergei Kucherenko, S. Kucherenko, 2013, Methods in molecular biology.

Influence of temperature, magnetic field, and high hydrostatic pressure on the resistivity and magnetoresistance in La0.9Mn1.1O3±δ ceramics and laser-deposited films

S. Kucherenko, V. Dyakonov, V. Pashchenko, 2001 .

Comparison of deterministic and stochastic approaches to global optimization

Leo Liberti, Sergei S. Kucherenko, Leo Liberti, 2005, Int. Trans. Oper. Res..

Metamodelling with independent and dependent inputs

Nilay Shah, Sergei S. Kucherenko, M. Munoz Zuniga, 2013, Comput. Phys. Commun..

Calculating first-order sensitivity measures: A benchmark of some recent methodologies

Stefano Tarantola, Marco Ratto, Sergei S. Kucherenko, 2009, Reliab. Eng. Syst. Saf..

Monte Carlo evaluation of derivative-based global sensitivity measures

Constantinos C. Pantelides, Nilay Shah, Maria Rodriguez-Fernandez, 2009, Reliab. Eng. Syst. Saf..

A flexible and generic approach to dynamic modelling of supply chains

Nilay Shah, Sergei S. Kucherenko, Nouri J. Samsatli, 2004, J. Oper. Res. Soc..

Sobol' indices for problems defined in non-rectangular domains

Nilay Shah, Sergei S. Kucherenko, Oleksiy V. Klymenko, 2016, Reliab. Eng. Syst. Saf..

Derivative-Based Global Sensitivity Measures and Their Link with Sobol' Sensitivity Indices

S. Kucherenko, S. Song, S. Kucherenko, 2016, MCQMC.

A Vine Copula-Based Global Sensitivity Analysis Method for Structures with Multidimensional Dependent Variables

S. Kucherenko, Shufang Song, Zhiwei Bai, 2021, Mathematics.

Influence of pressure, temperature, and magnetic field on the resistivity and magnetoresistive effect of lanthanum manganite ceramics and films with the composition La0.7Mn1.3O3±δ

S. Kucherenko, V. Pashchenko, A. A. Shemyakov, 2001 .

Dynamics of structural phase transition and changes in properties under the influence of elastically anisotropic deforming stresses, regularities of critical lines and points in magnetic semiconductors and magnetic dielectrics

S. Kucherenko, P. Polyakov, 2004 .

Global Sensitivity Analysis of Various Numerical Schemes for the Heston Model

Aneta Karaivanova, Emanouil Atanassov, Sergei Kucherenko, 2020, ICCS.

The T-P-H effects on the magnetoresistance of La0.7Ca0.3Mn1.0O3−δ ceramics and films

S. Kucherenko, V. P. D’yakonov, V. Pashchenko, 2001 .

Computationally efficient identification of probabilistic design spaces through application of metamodeling and adaptive sampling

Nilay Shah, Salvador García Muñoz, Sergei S. Kucherenko, 2020, Comput. Chem. Eng..

Application of Machine Learning and Global Sensitivity Analysis for Identification and Visualization of Design Space

Oleksiy Klymenko, Sergei Kucherenko, Nilay Shah, 2021, 31st European Symposium on Computer Aided Process Engineering.

Different numerical estimators for main effect global sensitivity indices

Sergei S. Kucherenko, Shufang Song, S. Kucherenko, 2016, Reliab. Eng. Syst. Saf..

Application of the control variate technique to estimation of total sensitivity indices

Stefano Tarantola, Bertrand Iooss, Sergei S. Kucherenko, 2015, Reliab. Eng. Syst. Saf..

Multivariate sensitivity analysis and derivative-based global sensitivity measures with dependent variables

Matieyendou Lamboni, Sergei S. Kucherenko, S. Kucherenko, 2021, Reliab. Eng. Syst. Saf..

Dynamics of light-induced diffraction gratings in silicon excited by picosecond pulses

S. Kucherenko, N. Kudryashov, A. N. Petrovskii, 1990 .