Доступ предоставлен для: Guest
International Journal for Uncertainty Quantification
Главный редактор: Habib N. Najm (open in a new tab)
Ассоциированный редакторs: Dongbin Xiu (open in a new tab) Tao Zhou (open in a new tab)
Редактор-основатель: Nicholas Zabaras (open in a new tab)

Выходит 6 номеров в год

ISSN Печать: 2152-5080

ISSN Онлайн: 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

A MULTI-FIDELITY STOCHASTIC COLLOCATION METHOD FOR PARABOLIC PARTIAL DIFFERENTIAL EQUATIONS WITH RANDOM INPUT DATA

Том 4, Выпуск 3, 2014, pp. 225-242
DOI: 10.1615/Int.J.UncertaintyQuantification.2014007778
Get accessDownload

Краткое описание

Over the last few years there have been dramatic advances in the area of uncertainty quantification. In particular, we have seen a surge of interest in developing efficient, scalable, stable, and convergent computational methods for solving differential equations with random inputs. Stochastic collocation (SC) methods, which inherit both the ease of implementation of sampling methods like Monte Carlo and the robustness of nonsampling ones like stochastic Galerkin to a great deal, have proved extremely useful in dealing with differential equations driven by random inputs. In this work we propose a novel enhancement to stochastic collocation methods using deterministic model reduction techniques. Linear parabolic partial differential equations with random forcing terms are analysed. The input data are assumed to be represented by a finite number of random variables. A rigorous convergence analysis, supported by numerical results, shows that the proposed technique is not only reliable and robust but also efficient.

ЦИТИРОВАНО В
  1. Raissi M., Seshaiyer P., Application of local improvements to reduced-order models to sampling methods for nonlinear PDEs with noise, International Journal of Computer Mathematics, 95, 5, 2018. Crossref

  2. Skinner Ryan, Doostan Alireza, Peters Eric, Evans John, Jansen Kenneth E., An Evaluation of Multi-Fidelity Modeling Efficiency on a Parametric Study of NACA Airfoils, 35th AIAA Applied Aerodynamics Conference, 2017. Crossref

  3. Giselle Fernández-Godino M., Park Chanyoung, Kim Nam H., Haftka Raphael T., Issues in Deciding Whether to Use Multifidelity Surrogates, AIAA Journal, 57, 5, 2019. Crossref

  4. Nguyen Long, Raissi Maziar, Seshaiyer Padmanabhan, Efficient Physics Informed Neural Networks Coupled with Domain Decomposition Methods for Solving Coupled Multi-physics Problems, in Advances in Computational Modeling and Simulation, 2022. Crossref

  5. Nguyen Long, Raissi Maziar, Seshaiyer Padmanabhan, Modeling, Analysis and Physics Informed Neural Network approaches for studying the dynamics of COVID-19 involving human-human and human-pathogen interaction, Computational and Mathematical Biophysics, 10, 1, 2022. Crossref

Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции Цены и условия подписки Begell House Контакты Language English 中文 Русский Português German French Spain