Testing the Sampling-Based NUSS-RF Tool for the Nuclear Data—Related Global Sensitivity Analysis with Monte Carlo Neutronics Calculations

Nuclear Science and Engineering ◽

10.13182/nse14-142 ◽

2016 ◽

Vol 184 (1) ◽

pp. 69-83 ◽

Author(s):

Ting Zhu ◽

Alexander Vasiliev ◽

Hakim Ferroukhi ◽

Dimitri Rochman ◽

Andreas Pautz

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo ◽

Global Sensitivity Analysis ◽

Nuclear Data ◽

Global Sensitivity

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Sensitivity Analysis in Diagnostic Modelling: Monte Carlo Filtering and Regionalised Sensitivity Analysis, Bayesian Uncertainty Estimation and Global Sensitivity Analysis

Sensitivity Analysis in Practice ◽

10.1002/0470870958.ch6 ◽

2004 ◽

pp. 151-192 ◽

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo ◽

Global Sensitivity Analysis ◽

Uncertainty Estimation ◽

Global Sensitivity

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Global Sensitivity Analysis for Power Systems via Quasi-Monte Carlo Methods

2019 4th International Conference on System Reliability and Safety (ICSRS) ◽

10.1109/icsrs48664.2019.8987666 ◽

2019 ◽

Author(s):

Jamie Fox ◽

Giray Okten ◽

Bahri Uzunoglu

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo ◽

Power Systems ◽

Monte Carlo Methods ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

Quasi Monte Carlo

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On global sensitivity analysis of quasi-Monte Carlo algorithms

Monte Carlo Methods and Applications ◽

10.1515/1569396054027274 ◽

2005 ◽

Vol 11 (1) ◽

Author(s):

I.M. Sobol´ ◽

S.S. Kucherenko

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

Quasi Monte Carlo ◽

Monte Carlo Algorithms

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Global Sensitivity Analysis of a Distributed Hydrologic Model Using Monte Carlo Simulation

World Environmental and Water Resources Congress 2010 ◽

10.1061/41114(371)482 ◽

2010 ◽

Author(s):

Tibebe Dessalegne ◽

Sharika U. S. Senarath ◽

Raul J. Novoa

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Global Sensitivity Analysis ◽

Hydrologic Model ◽

Global Sensitivity ◽

Distributed Hydrologic Model

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Pricing and Risk Management with High-Dimensional Quasi-Monte Carlo and Global Sensitivity Analysis

Wilmott ◽

10.1002/wilm.10434 ◽

2015 ◽

Vol 2015 (78) ◽

pp. 46-70 ◽

Author(s):

Marco Bianchetti ◽

Sergei Kucherenko ◽

Stefano Scoleri

Keyword(s):

Sensitivity Analysis ◽

Risk Management ◽

Monte Carlo ◽

Global Sensitivity Analysis ◽

High Dimensional ◽

Global Sensitivity ◽

Quasi Monte Carlo

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Global sensitivity analysis based on a Monte Carlo simulation: A study based on a natural gas system

Safety and Reliability of Complex Engineered Systems ◽

10.1201/b19094-337 ◽

2015 ◽

pp. 2571-2576

Author(s):

C Medeiros ◽

M Alencar ◽

T Garcez ◽

A de Almeida

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Natural Gas ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

Natural Gas System

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Monte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis

Sustainability ◽

10.3390/su9040539 ◽

2017 ◽

Vol 9 (4) ◽

pp. 539 ◽

Author(s):

Young-Jin Kim

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Global Sensitivity Analysis ◽

Global Sensitivity

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Erratum: “Quasi-Monte Carlo technique in global sensitivity analysis of wind resource assessment with a study on UAE” [J. Renewable Sustainable Energy 11, 053303 (2019)]

Journal of Renewable and Sustainable Energy ◽

10.1063/5.0007143 ◽

2020 ◽

Vol 12 (6) ◽

pp. 069901

Author(s):

Olga Tsvetkova ◽

Taha B. M. J. Ouarda

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo ◽

Global Sensitivity Analysis ◽

Sustainable Energy ◽

Resource Assessment ◽

Monte Carlo Technique ◽

Global Sensitivity ◽

Quasi Monte Carlo ◽

Wind Resource Assessment ◽

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Efficacy of quantifying marker-cluster rigidity in a multi-segment foot model: a Monte-Carlo based global sensitivity analysis and regression model

Computer Methods in Biomechanics & Biomedical Engineering ◽

10.1080/10255842.2021.1954170 ◽

2021 ◽

pp. 1-12

Author(s):

Po-Hsiang Chan ◽

Julie Stebbins ◽

Amy B. Zavatsky

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo ◽

Regression Model ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

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Randomized quasi-Monte Carlo methods in global sensitivity analysis

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.107520 ◽

2021 ◽

Vol 210 ◽

pp. 107520 ◽

Author(s):

Giray Ökten ◽

Yaning Liu

Keyword(s):

Sensitivity Analysis ◽

Monte Carlo ◽

Monte Carlo Methods ◽

Global Sensitivity Analysis ◽

Global Sensitivity ◽

Quasi Monte Carlo

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