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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 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

SPARSE MULTIRESOLUTION REGRESSION FOR UNCERTAINTY PROPAGATION

Volume 4, Numéro 4, 2014, pp. 303-331
DOI: 10.1615/Int.J.UncertaintyQuantification.2014010147
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RÉSUMÉ

The present work proposes a novel nonintrusive, i.e., sampling-based, framework for approximating stochastic solutions of interest admitting sparse multiresolution expansions. The coefficients of such expansions are computed via greedy approximation techniques that require a number of solution realizations smaller than the cardinality of the multiresolution basis. The effect of various random sampling strategies is investigated. The proposed methodology is verified on a number of benchmark problems involving nonsmooth stochastic responses, and is applied to quantifying the efficiency of a passive vibration control system operating under uncertainty.

CITÉ PAR
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  2. Hadigol Mohammad, Maute Kurt, Doostan Alireza, On uncertainty quantification of lithium-ion batteries: Application to an LiC6/LiCoO2 cell, Journal of Power Sources, 300, 2015. Crossref

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  5. Figueroa C. Alberto, Taylor Charles A., Marsden Alison L., Blood Flow, in Encyclopedia of Computational Mechanics Second Edition, 2017. Crossref

  6. Xu Juan, Zhang Jianjun, Sun Chunyu, Dong Jianghui, Feature extraction of vibration signal using OMP-NWE method, Journal of Vibroengineering, 19, 3, 2017. Crossref

  7. Couaillier Vincent, Savin Éric, Generalized Polynomial Chaos for Non-intrusive Uncertainty Quantification in Computational Fluid Dynamics, in Uncertainty Management for Robust Industrial Design in Aeronautics, 140, 2019. Crossref

  8. Hampton Jerrad, Doostan Alireza, Basis adaptive sample efficient polynomial chaos (BASE-PC), Journal of Computational Physics, 371, 2018. Crossref

  9. Diaz Paul, Doostan Alireza, Hampton Jerrad, Sparse polynomial chaos expansions via compressed sensing and D-optimal design, Computer Methods in Applied Mechanics and Engineering, 336, 2018. Crossref

  10. Pettersson Per, Doostan Alireza, Nordström Jan, Level set methods for stochastic discontinuity detection in nonlinear problems, Journal of Computational Physics, 392, 2019. Crossref

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  12. Kougioumtzoglou Ioannis A., Petromichelakis Ioannis, Psaros Apostolos F., Sparse representations and compressive sampling approaches in engineering mechanics: A review of theoretical concepts and diverse applications, Probabilistic Engineering Mechanics, 61, 2020. Crossref

  13. Seo Jongmin, Schiavazzi Daniele E., Kahn Andrew M., Marsden Alison L., The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls, International Journal for Numerical Methods in Biomedical Engineering, 36, 8, 2020. Crossref

  14. Inoue Takumi, Miyaji Koji, Non-Intrusive Uncertainty Quantification Method for Flows with Discontinuity, AIAA Scitech 2020 Forum, 2020. Crossref

  15. Savin Éric, Hantrais-Gervois Jean-Luc, Sparse polynomial surrogates for non-intrusive, high-dimensional uncertainty quantification of aeroelastic computations, Probabilistic Engineering Mechanics, 59, 2020. Crossref

  16. Wang Yu, Liu Fang, Schiavazzi Daniele E., Variational inference with NoFAS: Normalizing flow with adaptive surrogate for computationally expensive models, Journal of Computational Physics, 467, 2022. Crossref

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