Global sensitivity analysis for multivariate outputs using polynomial chaos-based surrogate models

DOI10.1016/j.apm.2020.02.005zbMath1481.62042OpenAlexW3007901103MaRDI QIDQ2174747

Yun Young Choi, Xiang Sun, Jung-il Choi

Publication date: 27 April 2020

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2020.02.005

zbMATH Keywords

proper orthogonal decompositionglobal sensitivity analysispolynomial chaosvector projectionmultivariate output

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Stochastic processes (60G99) Analysis of variance and covariance (ANOVA) (62J10)

Related Items

Non-intrusive reduced-order modeling for uncertainty quantification of space-time-dependent parameterized problems, Surrogate modeling for high dimensional uncertainty propagation via deep kernel polynomial chaos expansion, Multielement polynomial chaos kriging-based metamodelling for Bayesian inference of non-smooth systems, A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design, Novel algorithm for flexible multibody systems with hybrid uncertainties

Uses Software

• UQLab

Cites Work

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