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Matem. Mod., 1990, Volume 2, Number 1, Pages 112–118 (Mi mm2320)  

This article is cited in 12 scientific papers (total in 13 papers)

Computational methods and algorithms

On sensitivity estimation for nonlinear mathematical models

I. M. Sobol'

Keldysh Applied Mathematics Institute, Academy of Sciences of the USSR

Abstract: A theorem about decomposition of an integrable function into summands of different dimensions is proved. A Monte Carlo algorithm is proposed for estimating the sensitivity of a function with respect to arbitrary groups of variables.

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Bibliographic databases:
UDC: 519.676
Received: 05.07.1989

Citation: I. M. Sobol', “On sensitivity estimation for nonlinear mathematical models”, Matem. Mod., 2:1 (1990), 112–118

Citation in format AMSBIB
\by I.~M.~Sobol'
\paper On sensitivity estimation for nonlinear mathematical models
\jour Matem. Mod.
\yr 1990
\vol 2
\issue 1
\pages 112--118

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    This publication is cited in the following articles:
    1. I. M. Sobol, E. E. Myshetskaya, “Ob ispolzovanii kvazi-Monte-Karlo v otsenkakh bootstrap”, Matem. modelirovanie, 16:2 (2004), 118–122  mathnet  zmath
    2. I. M. Sobol, “Globalnye pokazateli chuvstvitelnosti dlya izucheniya nelineinykh matematicheskikh modelei”, Matem. modelirovanie, 17:9 (2005), 43–52  mathnet  mathscinet  zmath
    3. M. K. Kerimov, “On the 80th birthday of Il'ya Meierovich Sobol”, Comput. Math. Math. Phys., 47:7 (2007), 1065–1072  mathnet  crossref  mathscinet  elib
    4. Uspuras E., Kaliatka A., Kopustinskas V., Vaisnoras M., “Use of the Fast and Csm Methods for Analyzing Uncertainties in Hydraulic-Shock Modeling”, Atomic Energy, 109:3 (2011), 213–220  crossref  isi
    5. Campolongo F. Saltelli A. Cariboni J., “From Screening to Quantitative Sensitivity Analysis. a Unified Approach”, Comput. Phys. Commun., 182:4 (2011), 978–988  crossref  isi
    6. Xu J. Shu H. Jiang H. Dong L., “Sobol' Sensitivity Analysis of Parameters in the Common Land Model for Simulation of Water and Energy Fluxes”, Earth Sci. Inform., 5:3-4 (2012), 167–179  crossref  isi
    7. L. F. Nurislamova, I. M. Gubaidullin, “Issledovanie i redutsirovanie matematicheskoi modeli khimicheskoi reaktsii metodom Sobolya”, Kompyuternye issledovaniya i modelirovanie, 8:4 (2016), 633–646  mathnet
    8. Makai M., Vegh J., “Miscellaneous”: Makai, M Vegh, J, Reactor Core Monitoring: Background, Theory and Practical Applications, Lecture Notes in Energy, 58, Springer, 2017, 391–403  isi
    9. Makai M., Vegh J., “Parameter Fitting, Sensitivity, Stability”: Makai, M Vegh, J, Reactor Core Monitoring: Background, Theory and Practical Applications, Lecture Notes in Energy, 58, Springer, 2017, 405–413  isi
    10. Nikishova A., Kalyuzhnaya A., Boukhanovsky A., Hoekstra A., “Uncertainty Quantification and Sensitivity Analysis Applied to the Wind Wave Model Swan”, Environ. Modell. Softw., 95 (2017), 344–357  crossref  isi
    11. Oden J.T., “Adaptive Multiscale Predictive Modelling”, Acta Numer., 27 (2018), 353–450  crossref  isi
    12. Nikishova A., Veen L., Zun P., Hoekstra A.G., “Uncertainty Quantification of a Multiscale Model For in-Stent Restenosis”, Cardiovasc. Eng. Technol., 9:4, SI (2018), 761–774  crossref  isi  scopus
    13. Ballester-Ripoll R., Paredes E.G., Pajarola R., “Sobol Tensor Trains For Global Sensitivity Analysis”, Reliab. Eng. Syst. Saf., 183 (2019), 311–322  crossref  isi  scopus
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