Derivative-Based Global Sensitivity Analysis for Models with High-Dimensional Inputs and Functional Outputs

DOI10.1137/19M1243518zbMath1482.65007arXiv1902.04630WikidataQ126797177 ScholiaQ126797177MaRDI QIDQ5243531

Helen Cleaves, Alen Alexanderian, Hayley Guy, Meilin Yu, Ralph C. Smith

Publication date: 18 November 2019

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.04630

zbMATH Keywords

global sensitivity analysisKarhunen-Loéve expansionsDGSMsfunctional Sobol' indices

Mathematics Subject Classification ID

Multivariate analysis (62H99) Probabilistic models, generic numerical methods in probability and statistics (65C20) Computational methods for problems pertaining to probability theory (60-08) Algorithms for approximation of functions (65D15)

Related Items (3)

A distributed active subspace method for scalable surrogate modeling of function valued outputs ⋮ A generalized sensitivity analysis method based on variance and covariance decomposition of summatory functions for multi-input multi-output systems ⋮ Structure exploiting methods for fast uncertainty quantification in multiphase flow through heterogeneous media

Cites Work

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• Active Subspaces
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• Erratum: Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces
• Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions
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• Efficient Computation of Sobol' Indices for Stochastic Models
• Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
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