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

Thumbnail Image

Files

1902.04630.pdf (1.88 MB)

Links to Files

https://arxiv.org/pdf/1902.04630.pdf

Permanent Link

http://hdl.handle.net/11603/12948

Collections

UMBC Mechanical Engineering Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

Date

2019-02-17

Type of Work

journal articles preprints
Text

Department

Program

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.pdf

Rights

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 analysis
derivative-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