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• W2913607668 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." @default.
• W2913607668 created "2019-02-21" @default.
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• W2913607668 date "2019-02-12" @default.
• W2913607668 modified "2023-09-24" @default.
• W2913607668 title "Derivative-based global sensitivity analysis for models with high-dimensional inputs and functional outputs" @default.
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• W2913607668 doi "https://doi.org/10.48550/arxiv.1902.04630" @default.
• W2913607668 hasPublicationYear "2019" @default.
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