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Matematicheskoe modelirovanie, 2007, Volume 19, Number 11, Pages 23–24 (Mi mm1208)  

This article is cited in 33 scientific papers (total in 33 papers)

Global sensitivity indices for the investigation of nonlinear mathematical models

I. M. Sobol'

Institute for Mathematical Modelling, Russian Academy of Sciences
References:
Received: 13.07.2007
Bibliographic databases:
Language: Russian
Citation: I. M. Sobol', “Global sensitivity indices for the investigation of nonlinear mathematical models”, Mat. Model., 19:11 (2007), 23–24
Citation in format AMSBIB
\Bibitem{Sob07}
\by I.~M.~Sobol'
\paper Global sensitivity indices for the investigation of nonlinear mathematical models
\jour Mat. Model.
\yr 2007
\vol 19
\issue 11
\pages 23--24
\mathnet{http://mi.mathnet.ru/mm1208}
\mathscinet{http://mathscinet.ams.org/mathscinet-getitem?mr=2494563}
\zmath{https://zbmath.org/?q=an:1140.60323}
Linking options:
  • https://www.mathnet.ru/eng/mm1208
  • https://www.mathnet.ru/eng/mm/v19/i11/p23
    Addendum to
    This publication is cited in the following 33 articles:
    1. Tan J., Maleki P., An L., Di Luigi M., Villa U., Zhou Ch., Ren Sh., Faghihi D., “A Predictive Multiphase Model of Silica Aerogels For Building Envelope Insulations”, Comput. Mech., 69:6 (2022), 1457–1479  crossref  mathscinet  isi
    2. Kamalov F., “Orthogonal Variance Decomposition Based Feature Selection”, Expert Syst. Appl., 182 (2021), 115191  crossref  isi  scopus
    3. Lima Ernesto A. B. F., Faghihi D., Philley R., Yang J., Virostko J., Phillips C.M., Yankeelov T.E., “Bayesian Calibration of a Stochastic, Multiscale Agent-Based Model For Predicting in Vitro Tumor Growth”, PLoS Comput. Biol., 17:11 (2021), e1008845  crossref  mathscinet  isi
    4. Tan J., Villa U., Shamsaei N., Shao Sh., Zbib H.M., Faghihi D., “A Predictive Discrete-Continuum Multiscale Model of Plasticity With Quantified Uncertainty”, Int. J. Plast., 138 (2021), 102935  crossref  isi
    5. Sun X., Croke B., Roberts S., Jakeman A., “Comparing Methods of Randomizing Sobol' Sequences For Improving Uncertainty of Metrics in Variance-Based Global Sensitivity Estimation”, Reliab. Eng. Syst. Saf., 210 (2021), 107499  crossref  mathscinet  isi  scopus
    6. Lamboni M., “Uncertainty Quantification: a Minimum Variance Unbiased (Joint) Estimator of the Non-Normalized Sobol' Indices”, Stat. Pap., 61:5 (2020), 1939–1970  crossref  mathscinet  isi  scopus
    7. Nikishova A. Comi G.E. Hoekstra A.G., “Sensitivity Analysis Based Dimension Reduction of Multiscale Models”, Math. Comput. Simul., 170 (2020), 205–220  crossref  mathscinet  isi
    8. Caccavale O.M. Giuffrida V., “The Proteus Composite Index: Towards a Better Metric For Global Food Security”, World Dev., 126 (2020), 104709  crossref  isi
    9. Koosha R., Shahsavari F., Proceedings of the Asme International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2019, Vol 1, Amer Soc Mechanical Engineers, 2020  isi
    10. Koosha R., Shahsavari F., Proceedings of the Asme International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2019, Vol 1, Amer Soc Mechanical Engineers, 2020  isi
    11. Qian G., Mahdi A., “Sensitivity Analysis Methods in the Biomedical Sciences”, Math. Biosci., 323 (2020), 108306  crossref  mathscinet  isi  scopus
    12. Lucay F.A., Lopez-Arenas T., Sales-Cruz M., Galvez E.D., Cisternas L.A., “Performance Profiles For Benchmarking of Global Sensitivity Analysis Algorithms”, Rev. Mex. Ing. Quim., 19:1 (2020), 423–444  crossref  isi
    13. Puy A., Lo Piano S., Saltelli A., “A Sensitivity Analysis of the Pawn Sensitivity Index”, Environ. Modell. Softw., 127 (2020)  crossref  isi
    14. Bhattacharyya B., “Global Sensitivity Analysis: a Bayesian Learning Based Polynomial Chaos Approach”, J. Comput. Phys., 415 (2020), 109539  crossref  mathscinet  isi  scopus
    15. Li Zh., Liu W., Ming P., An P., Lu W., Pan J., “The Sensitivity Analysis of the Core Lower Head Molten Pool Model Based on Variance Decomposition”, J. Nucl. Eng. Radiat. Sci., 6:4 (2020), 041103  crossref  isi  scopus
    16. Braband M., Adams M., Wilhelmi A., Scherer M., “Global Sensitivity Analysis on the Torque Accuracy of the Powertrain in Electric Vehicles”, IFAC PAPERSONLINE, 53:2 (2020), 14067–14072  crossref  isi
    17. Zhang P., “A Novel Feature Selection Method Based on Global Sensitivity Analysis With Application in Machine Learning-Based Prediction Model”, Appl. Soft. Comput., 85 (2019), 105859  crossref  isi  scopus
    18. Oden J.T., “Adaptive Multiscale Predictive Modelling”, Acta Numer., 27 (2018), 353–450  crossref  isi
    19. Moeindarbari H., Taghikhany T., “Seismic Reliability Assessment of Base-Isolated Structures Using Artificial Neural Network: Operation Failure of Sensitive Equipment”, Earthq. Struct., 14:5 (2018), 425–436  crossref  isi
    20. Yun W., Lu Zh., Jiang X., “An Efficient Sampling Approach For Variance-Based Sensitivity Analysis Based on the Law of Total Variance in the Successive Intervals Without Overlapping”, Mech. Syst. Signal Proc., 106 (2018), 495–510  crossref  isi
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
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