Adaptive sparse polynomial dimensional decomposition for derivative-based sensitivity

Tang, Kunkun; Wang, Jonathan M.; Freund, Jonathan B.

doi:10.1016/j.jcp.2019.04.042

Adaptive sparse polynomial dimensional decomposition for derivative-based sensitivity

Journal Article · 23 April 2019 · Journal of Computational Physics

DOI:https://doi.org/10.1016/j.jcp.2019.04.042· OSTI ID:1614491

^[1]; Wang, Jonathan M. ^[2]; Freund, Jonathan B. ^[2]

University of Illinois at Urbana-Champaign, IL (United States); DOE/OSTI
University of Illinois at Urbana-Champaign, IL (United States)

In this report applications, such as the plasma-coupled combustion system we consider, derivative-based sensitivity indices (DSI) are known to have several advantages over Sobol's total sensitivity indices, especially for small sample sizes. Several properties of derivative-based sensitivity measures are leveraged to develop a new and efficient numerical approach to estimate them. It is based on computing the DSI measures by effectively cost-free Monte Carlo sampling of an adaptively constructed orthogonal polynomial surrogate with uncertain input parameters that can have arbitrary probability distributions. The adaptivity reduces the number of necessary model evaluations, which is demonstrated both in a constructed example (the Moon function) and in two plasma-combustion systems with up to 55 uncertain parameters. Unimportant parameters are successfully identified and neglected with a low number of model evaluations, which makes it an attractive non-intrusive approach when adjoint solutions are unavailable to provide sensitivity information.

View Accepted Manuscript (DOE)

Research Organization:: University of Illinois at Urbana-Champaign, IL (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0002374

OSTI ID:: 1614491

Alternate ID(s):: OSTI ID: 1636017

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Journal Issue: C Vol. 391; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

42 ENGINEERING
ANOVA
PDD
derivative-based sensitivity indices
global sensitivity analysis
high dimensionality
non-intrusive sensitivity
polynomial dimensional decomposition

Adaptive sparse polynomial dimensional decomposition for derivative-based sensitivity

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