Cleaves, Helen L.Alexanderian, AlenGuy, HayleySmith, Ralph C.Yu, Meilin2019-03-062019-03-062019-02-17Helen 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.pdfhttp://hdl.handle.net/11603/12948We 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- port26 pagesen-USThis 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.global sensitivity analysisderivative-based global sensitivity measures (DGSMs)functional Sobol' indicesKarhunen-Loeve expansionsText