Inscrição na biblioteca: Guest
International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

Ganhar acesso(open in a new tab)
Mais
download de artigo Download do registro MARC(open in a new tab) Obter permissões(open in a new tab)

UNCERTAINTIES ASSESSMENT IN GLOBAL SENSITIVITY INDICES ESTIMATION FROM METAMODELS

Volume 4, Edição 1, 2014, pp. 21-36
DOI: 10.1615/Int.J.UncertaintyQuantification.2012004291
Get accessDownload
Alexandre Janon
Maelle Nodet
Joseph Fourier University, Laboratoire Jean Kuntzmann, MOISE team, BP 53, 38041 Grenoble Cedex, France
Clementine Prieur
Université Grenoble Alpes, CNRS, LJK, F-38000 Grenoble, France; Inria Project/Team AIRSEA, France

RESUMO

Global sensitivity analysis is often impracticable for complex and resource intensive numerical models, as it requires a large number of runs. The metamodel approach replaces the original model by an approximated code that is much faster to run. This paper deals with the information loss in the estimation of sensitivity indices due to the metamodel approximation. A method for providing a robust error assessment is presented, hence enabling significant time savings without sacrificing precision and rigor. The methodology is illustrated for two different types of metamodels: one based on reduced basis, the other one on reproducing Kernel Hilbert space (RKHS) interpolation.

Palavras-chave: sensitivity analysis, Sobol indices, bootstrap method, Monte Carlo, response surface method
CITADO POR

  1. Le Gratiet Loic, Cannamela Claire, Iooss Bertrand, A Bayesian Approach for Global Sensitivity Analysis of (Multifidelity) Computer Codes, SIAM/ASA Journal on Uncertainty Quantification, 2, 1, 2014. Crossref

  2. Janon Alexandre, Klein Thierry, Lagnoux Agnès, Nodet Maëlle, Prieur Clémentine, Asymptotic normality and efficiency of two Sobol index estimators, ESAIM: Probability and Statistics, 18, 2014. Crossref

  3. Guenther John, Lee Herbert K. H., Gray Genetha A., Sequential design for achieving estimated accuracy of global sensitivities, Applied Stochastic Models in Business and Industry, 31, 6, 2015. Crossref

  4. Janon Alexandre, Nodet Maëlle, Prieur Christophe, Prieur Clémentine, Global sensitivity analysis for the boundary control of an open channel, Mathematics of Control, Signals, and Systems, 28, 1, 2016. Crossref

  5. Girard Sylvain, Mallet Vivien, Korsakissok Irène, Mathieu Anne, Emulation and Sobol' sensitivity analysis of an atmospheric dispersion model applied to the Fukushima nuclear accident, Journal of Geophysical Research: Atmospheres, 121, 7, 2016. Crossref

  6. Rohmer J., Lecacheux S., Pedreros R., Quetelard H., Bonnardot F., Idier D., Dynamic parameter sensitivity in numerical modelling of cyclone-induced waves: a multi-look approach using advanced meta-modelling techniques, Natural Hazards, 84, 3, 2016. Crossref

  7. Hart J. L., Alexanderian A., Gremaud P. A., Efficient Computation of Sobol' Indices for Stochastic Models, SIAM Journal on Scientific Computing, 39, 4, 2017. Crossref

  8. Qian E., Peherstorfer B., O'Malley D., Vesselinov V. V., Willcox K., Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices, SIAM/ASA Journal on Uncertainty Quantification, 6, 2, 2018. Crossref

  9. Hu Zhen, Mahadevan Sankaran, Probability models for data-Driven global sensitivity analysis, Reliability Engineering & System Safety, 187, 2019. Crossref

  10. Xu Hongyi, Liu Zhao, Control Variate Multifidelity Estimators for the Variance and Sensitivity Analysis of Mesostructure–Structure Systems, ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg, 5, 2, 2019. Crossref

  11. Antoniano-Villalobos Isadora, Borgonovo Emanuele, Lu Xuefei, Nonparametric estimation of probabilistic sensitivity measures, Statistics and Computing, 30, 2, 2020. Crossref

  12. Karagiannis Georgios, Hao Wenrui, Lin Guang, Calibrations and validations of biological models with an application on the renal fibrosis, International Journal for Numerical Methods in Biomedical Engineering, 36, 5, 2020. Crossref

  13. Rohmer Jeremy, Idier Deborah, Pedreros Rodrigo, A nuanced quantile random forest approach for fast prediction of a stochastic marine flooding simulator applied to a macrotidal coastal site, Stochastic Environmental Research and Risk Assessment, 34, 6, 2020. Crossref

  14. Razavi Saman, Jakeman Anthony, Saltelli Andrea, Prieur Clémentine, Iooss Bertrand, Borgonovo Emanuele, Plischke Elmar, Lo Piano Samuele, Iwanaga Takuya, Becker William, Tarantola Stefano, Guillaume Joseph H.A., Jakeman John, Gupta Hoshin, Melillo Nicola, Rabitti Giovanni, Chabridon Vincent, Duan Qingyun, Sun Xifu, Smith Stefán, Sheikholeslami Razi, Hosseini Nasim, Asadzadeh Masoud, Puy Arnald, Kucherenko Sergei, Maier Holger R., The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support, Environmental Modelling & Software, 137, 2021. Crossref

  15. Lesieur Antoine, Aumond Pierre, Mallet Vivien, Can Arnaud, Meta-modeling for urban noise mapping, The Journal of the Acoustical Society of America, 148, 6, 2020. Crossref

  16. Panin Ivan, Risk of estimators for Sobol’ sensitivity indices based on metamodels, Electronic Journal of Statistics, 15, 1, 2021. Crossref

  17. Castellan Gwenaëlle, Cousien Anthony, Tran Viet Chi, Non-parametric adaptive estimation of order 1 Sobol indices in stochastic models, with an application to Epidemiology, Electronic Journal of Statistics, 14, 1, 2020. Crossref

  18. Zhao Hu, Amann Florian, Kowalski Julia, Emulator-based global sensitivity analysis for flow-like landslide run-out models, Landslides, 18, 10, 2021. Crossref

  19. Pagani Stefano, Manzoni Andrea, Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning, International Journal for Numerical Methods in Biomedical Engineering, 37, 6, 2021. Crossref

  20. Kapusuzoglu Berkcan, Mahadevan Sankaran, Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data, Reliability Engineering & System Safety, 214, 2021. Crossref

1170 Visualizações do artigo 1659 downloads de artigos Métricas
1170 VISUALIZAÇÕES 1659 TRANSFERÊNCIAS 20 Crossref CITAÇÕES Google
Scholar CITAÇÕES

Artigos com conteúdo semelhante:

Land Cover Changes Analysis Based on Deep Machine Learning Technique Journal of Automation and Information Sciences, Vol.48, 2016, issue 5
Nikolay S. Lavreniuk , Nataliya N. Kussul, Bogdan Ya. Yailymov, Igor N. Butko , Andrey Yu. Shelestov
MODEL REDUCTION FOR LARGE-SCALE EARTHQUAKE SIMULATION IN AN UNCERTAIN 3D MEDIUM International Journal for Uncertainty Quantification, Vol.10, 2020, issue 2
O. Le Maître, Pierre Sochala, F. De Martin
Analysis of Computational Schemes for Geohydrodynamics Processes Modeling Journal of Automation and Information Sciences, Vol.41, 2009, issue 8
Vasiliy V. Skopetsky, Vsevolod A. Bohaienko, Vladimir I. Kudin
EMBEDDED MULTILEVEL MONTE CARLO FOR UNCERTAINTY QUANTIFICATION IN RANDOM DOMAINS International Journal for Uncertainty Quantification, Vol.11, 2021, issue 1
Santiago Badia, Javier Principe, Jerrad Hampton
Solution of Certain Initial Control Problems by the Optimal Set Partition Method Journal of Automation and Information Sciences, Vol.29, 1997, issue 4-5
Vladimir E. Kapustyan, Elena M. Kiseleva, L. S. Krokha

Última edição

MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS Ferdinand Uilhoorn DECISION THEORETIC BOOTSTRAPPING Peyman Tavallali, Hamed Hamze Bajgiran, Danial J. Esaid, Houman Owhadi UNCERTAINTY QUANTIFICATION AND GLOBAL SENSITIVITY ANALYSIS OF SEISMIC FRAGILITY CURVES USING KRIGING Clement Gauchy, C. Feau, Josselin Garnier STOCHASTIC GALERKIN METHOD AND PORT-HAMILTONIAN FORM FOR LINEAR FIRST-ORDER ORDINARY DIFFERENTIAL EQUATIONS Roland Pulch, Olivier Sète EXTREME LEARNING MACHINES FOR VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS John E. Darges, Alen Alexanderian, Pierre A. Gremaud

Próximos artigos

Uncertainty Analysis for Drift-Diffusion Equations Greta Marino, Jan-Frederik Pietschmann, Alois Pichler Maximizing Regional Sensitivity Analysis indices to find sensitive model behaviors Sebastien Roux, Patrice Loisel, Samuel Buis Measuring inputs-outputs association for time-depending hazard models under safety objectives using kernels Matieyendou LAMBONI Application of global sensitivity analysis for identification of probabilistic design spaces Sergei Kucherenko, Dimitris Giamalakis, Nilay Shah SENSITIVITY ANALYSES OF A MULTI-PHYSICS LONG-TERM CLOGGING MODEL FOR STEAM GENERATORS Edgar Jaber, Vincent Chabridon, Emmanuel Remy, Michaël Baudin, Didier Lucor, Mathilde Mougeot, Bertrand Iooss Analysis of the Challenges in Developing Sample-Based Multi-fidelity Estimators for Non-deterministic Models Bryan Reuter, Gianluca Geraci, Timothy Wildey
Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain