Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs
Files
Links to Files
https://arxiv.org/pdf/1902.04630.pdfPermanent Link
http://hdl.handle.net/11603/12948Metadata
Show full item recordDate
2019-02-17Type of Work
26 pagesText
journal articles preprints
Citation of Original Publication
Helen 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.pdfRights
This 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.Subjects
global sensitivity analysisderivative-based global sensitivity measures (DGSMs)
functional Sobol' indices
Karhunen-Loeve expansions
Abstract
We 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-
port