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arXiv · 1902.04630

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

Helen L. Cleaves·Alen Alexanderian·Hayley Guy·Ralph C. Smith·Meilin Yu
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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 field representation via Karhunen--Lo\`{e}ve expansions, and adjoint-based gradient computation to provide a scalable computational framework for computing the proposed 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 biotransport.

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Helen L. Cleaves, Alen Alexanderian, Hayley Guy, Ralph C. Smith, Meilin Yu. 2019-02-12. Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs. https://arxiv.org/abs/1902.04630

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