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Sensitivity analysis of model output: variance-based methods make the difference

Authors:

Karen Chan,

Andrea Saltelli,

Stefano TarantolaAuthors Info & Claims

WSC '97: Proceedings of the 29th conference on Winter simulation

Pages 261 - 268

https://doi.org/10.1145/268437.268489

Published: 01 December 1997 Publication History

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https://doi.org/10.1016/j.envsoft.2024.106189
Todorov VGeorgiev SGeorgiev IZaharieva SDimov I(2023)Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM FrameworkMathematics10.3390/math1113291911:13(2919)Online publication date: 29-Jun-2023
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Index Terms

Sensitivity analysis of model output: variance-based methods make the difference
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
    2. Simulation evaluation
2. Theory of computation
  1. Logic
  2. Models of computation
    1. Computability

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Published In

WSC '97: Proceedings of the 29th conference on Winter simulation

December 1997

1452 pages

ISBN:078034278X

Editors:
Sigrún Andradóttir
Georgia Institute of Technology, Atlanta
,
Kevin J. Healy
Thread Technologies, West Lafayette, IN
,
David H. Withers
Dell Computer Co., Round Rock, TX
,
Barry L. Nelson
Northwestern Univ., Evanston, IL

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IEEE Computer Society

United States

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Published: 01 December 1997

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WSC97: 1997 Winter Simulation Conference

December 7 - 10, 1997

Georgia, Atlanta, USA

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WSC '97 Paper Acceptance Rate 121 of 191 submissions, 63%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Ju BSon HLee J(2024)Advanced Sobol sensitivity analysis of a 1:4-scale prestressed concrete containment vessel using an ANN-based surrogate modelNuclear Engineering and Technology10.1016/j.net.2024.10.021Online publication date: Oct-2024
https://doi.org/10.1016/j.net.2024.10.021
Wang LXu YXu JGu HBai ZZhou PYu HGuo Y(2024)Increasing parameter identifiability through clustered time-varying sensitivity analysisEnvironmental Modelling & Software10.1016/j.envsoft.2024.106189(106189)Online publication date: Aug-2024
https://doi.org/10.1016/j.envsoft.2024.106189
Todorov VGeorgiev SGeorgiev IZaharieva SDimov I(2023)Optimizing Air Pollution Modeling with a Highly-Convergent Quasi-Monte Carlo Method: A Case Study on the UNI-DEM FrameworkMathematics10.3390/math1113291911:13(2919)Online publication date: 29-Jun-2023
https://doi.org/10.3390/math11132919
Dimov ITodorov VGeorgiev S(2023)A Super-Convergent Stochastic Method Based on the Sobol Sequence for Multidimensional Sensitivity Analysis in Environmental ProtectionAxioms10.3390/axioms1202014612:2(146)Online publication date: 31-Jan-2023
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Liu BPenaka SLu WFeng KRebbling AOlofsson T(2023)Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern SwedenTechnology in Society10.1016/j.techsoc.2023.10234775(102347)Online publication date: Nov-2023
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Acheampong JGyamfi CArthur E(2023)Impacts of retention basins on downstream flood peak attenuation in the Odaw river basin, GhanaJournal of Hydrology: Regional Studies10.1016/j.ejrh.2023.10136447(101364)Online publication date: Jun-2023
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Todorov VDimov I(2022)Innovative Digital Stochastic Methods for Multidimensional Sensitivity Analysis in Air Pollution ModellingMathematics10.3390/math1012214610:12(2146)Online publication date: 20-Jun-2022
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Ignatowicz KSolaï EMorency FBeaugendre H(2022)Data-Driven Calibration of Rough Heat Transfer Prediction Using Bayesian Inversion and Genetic AlgorithmEnergies10.3390/en1510379315:10(3793)Online publication date: 21-May-2022
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Wei XZhang JZhou HŽivanović S(2022)Sensitivity Analysis for Pedestrian-Induced Vibration in FootbridgesBuildings10.3390/buildings1207088312:7(883)Online publication date: 22-Jun-2022
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Distribution-based sensitivity analysis from a generic input-output sample

Non-parametric methods for global sensitivity analysis of model output with dependent inputs

Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model

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Distribution-based sensitivity analysis from a generic input-output sample

Non-parametric methods for global sensitivity analysis of model output with dependent inputs

Comparison of variance-based and moment-independent global sensitivity analysis approaches by application to the SWAT model

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