This paper addresses the sensitivity analysis of time-dependent computer models. Often, in practice, we partition the inputs into a subset of inputs relevant to the application studied, and a complement subset of nuisance inputs that are not of interest. We propose sensitivity measures for the relevant inputs of such dynamic computer models. The subset of nuisance inputs is used to create replication-type information to help quantify the uncertainty of sensitivity measures (or indices) for the relevant inputs. The method is first demonstrated on an analytical example. Then we use the proposed method in an application about the safety of restraint systems in light tactical vehicles. The method indicates that chest deflection curves are more sensitive to the addition of pretensioners and load limiters than to the type of seatbelt.
Uncertainty Quantification of Complement Sensitivity Indices in Dynamic Computer Models
Manuscript received August 17, 2018; final manuscript received February 20, 2019; published online April 15, 2019. Assoc. Editor: Sankaran Mahadevan.
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Drignei, D., Mourelatos, Z., and Hu, Z. (April 15, 2019). "Uncertainty Quantification of Complement Sensitivity Indices in Dynamic Computer Models." ASME. ASME J. Risk Uncertainty Part B. June 2019; 5(2): 020901. https://doi.org/10.1115/1.4042924
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