September 1, 2019 Software Open Access

drojasd

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  <identifier identifierType="DOI">10.5281/zenodo.3383316</identifier>
  <creators>
    <creator>
      <creatorName>drojasd</creatorName>
      <affiliation></affiliation>
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  <titles>
    <title>drojasd/GSUA-CSB: GSUA-CSB v1.0</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <dates>
    <date dateType="Issued">2019-09-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3383316</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/drojasd/GSUA-CSB/tree/GSUA-CSB</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3383315</relatedIdentifier>
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  <version>GSUA-CSB</version>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Global Sensitivity and Uncertainty Analysis - Uncertainty Confidence Interval (GSUA-UCI) Toolbox is a product developed by Universidad EAFIT for command-line mathematical model validation in both of Simulink or Symbolic Math Toolbox environment . At present time, the toolbox allows to perform the following functions: To apply and visualize several variance-based sensitivity (SA) and uncertainty (UA) analysis, to estimate model parameters (PE) and to estimate confidence subcontour box (CSB) for estimated parameters. This toolbox is based on the previous work of Carlos Mario Vélez: GSUA of dynamical systems using variance-based methods.&lt;/p&gt;
&lt;p&gt;Sensitivity indices estimators implemented in this toolbox are based on the following works:
[1]: Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and Tarantola, S. (2010). Variance based sensitivity analysis of model output. design and estimator for the total sensitivity index. Computer Physics Communications, 181(2):259–270.
[2]: Xiao, S., Lu, Z., and Wang, P. (2018). Multivariate global sensitivity analysis based on distance components decomposition. Risk Analysis, 38(12):2703–2721.&lt;/p&gt;</description>
  </descriptions>
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