Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs

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dc.contributor.authorCleaves, Helen L.
dc.contributor.authorAlexanderian, Alen
dc.contributor.authorGuy, Hayley
dc.contributor.authorSmith, Ralph C.
dc.contributor.authorYu, Meilin
dc.date.accessioned2019-03-06T14:49:19Z
dc.date.available2019-03-06T14:49:19Z
dc.date.issued2019-02-17
dc.description.abstractWe present a framework for derivative-based global sensitivity analysis (GSA) for models with high-dimensional input parameters and functional outputs. We combine ideas from derivative-based GSA, random eld representation via Karhunen-Loeve expansions, and adjoint- based gradient computation to provide a scalable computational framework for computing the pro- posed derivative-based GSA measures. We illustrate the strategy for a nonlinear ODE model of cholera epidemics and for elliptic PDEs with application examples from geosciences and biotrans- porten_US
dc.description.sponsorshipThe research of A. Alexanderian and R.C. Smith was partially supported by the National Science Foundation through the grant DMS-1745654. The research of R.C. Smith was supported in part by the Air Force O ce of Scienti c Research (AFOSR) through the grant AFOSR FA9550-15-1-0299. M.L. Yu gratefully acknowledge the faculty startup support from the department of mechanical engineering at the University of Maryland, Baltimore County (UMBC).en_US
dc.description.urihttps://arxiv.org/pdf/1902.04630.pdfen_US
dc.format.extent26 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m20oe7-3jrg
dc.identifier.citationHelen L. Cleaves, Alen Alexanderian, Hayley Guy, Ralph C. Smith and Meilin Yu, Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs, 2019, https://arxiv.org/pdf/1902.04630.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/12948
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mechanical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectglobal sensitivity analysisen_US
dc.subjectderivative-based global sensitivity measures (DGSMs)en_US
dc.subjectfunctional Sobol' indicesen_US
dc.subjectKarhunen-Loeve expansionsen_US
dc.titleDerivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputsen_US
dc.typeTexten_US

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