Uncertainty quantification through the Monte Carlo method in a cloud computing setting

The Monte Carlo (MC) method is the most common technique used for uncertainty quantification, due to its simplicity and good statistical results. However, its computational cost is extremely high, and, in many cases, prohibitive. Fortunately, the MC algorithm is easily parallelizable, which allows i...

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Bibliographic Details
Published in:	Computer physics communications Vol. 185; no. 5; pp. 1355 - 1363
Main Authors:	Cunha, Americo, Nasser, Rafael, Sampaio, Rubens, Lopes, Hélio, Breitman, Karin
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.05.2014 Elsevier
Subjects:	Cloud computing Computation Computer Aided Engineering Computer Science Computer simulation Data Structures and Algorithms Distributed, Parallel, and Cluster Computing MapReduce Mathematics Methodology Monte Carlo method Monte Carlo methods Parallel algorithm Parallel processing Probability Statistics Strategy Uncertainty Uncertainty quantification Uncertainty quantification Cloud computing Monte Carlo method Parallel algorithm MapReduce uncertainty quantification cloud computing parallel algorithm
ISSN:	0010-4655, 1879-2944
Online Access:	Get full text
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