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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 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

MULTILEVEL MONTE CARLO SAMPLING ON HETEROGENEOUS COMPUTER ARCHITECTURES

Volume 10, Issue 6, 2020, pp. 575-594
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033179
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ABSTRACT

Monte Carlo (MC) sampling is the standard approach for uncertainty propagation in problems with high-dimensional stochastic inputs. Various acceleration techniques have been developed to overcome the slow convergence of MC estimates, such as multilevel Monte Carlo (MLMC). MLMC uses successive approximations computed on levels, models with different levels of accuracy, and computational cost to reduce the estimator variance. MLMC analytically determines the number of samples required on each level to achieve a given accuracy at minimal cost. We propose an extension of the original MLMC theoretical framework for modern, heterogeneous computer architectures in which accelerators (GPUs) are available and, therefore, samples can be distributed on both different levels and different compute units (CPUs and GPUs). We derive the optimal sample allocation for the proposed MLMC extension by solving a convex optimization problem. We apply the MLMC extension to a stochastically heated channel flow to provide insight for a study on the design of concentrated solar energy receivers. We demonstrate for the stochastically heated channel flow that the proposed MLMC extension leads to considerable total cost reduction (up to 86%) compared to MLMC using only GPUs.

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CITED BY
  1. Valero Mario Miguel, Jofre Lluís, Torres Ricardo, Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis, Environmental Modelling & Software, 141, 2021. Crossref

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