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ArticleGlobal sensitivity analysis via multi-fidelity polynomial chaos expansion

Pramudita Satria Palar2017

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Articlegreen2017

Global sensitivity analysis via multi-fidelity polynomial chaos expansion

Pramudita Satria Palar · Lavi Rizki Zuhal · Koji Shimoyama · Takeshi Tsuchiya

80 citations·0/0 files stored·Reliability Engineering & System Safety

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Probabilistic and Robust Engineering Design Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference

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References

1992STATISTICS FOR SPATIAL DATA 1989Design and Analysis of Computer Experiments 1991Stochastic Finite Elements: A Spectral Approach 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations 2007Global sensitivity analysis using polynomial chaos expansions 2011AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation 1996Importance measures in global sensitivity analysis of nonlinear models 2005High-Order Collocation Methods for Differential Equations with Random Inputs

Cited by

2022An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis 2018Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices 2019Efficient methods by active learning Kriging coupled with variance reduction based sampling methods for time-dependent failure probability 2022Uncertainty quantification and global sensitivity analysis of composite wind turbine blades 2022Deep adaptive arbitrary polynomial chaos expansion: A mini-data-driven semi-supervised method for uncertainty quantification

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Details

Year2017

Typearticle

Published2017-10-27

VenueReliability Engineering & System Safety

ISSN-L0951-8320

Languageen

Citations80

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.ress.2017.10.013

openalex_workW2963077925

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2019Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression

2024AMFGP: An active learning reliability analysis method based on multi-fidelity Gaussian process surrogate model

2019Multi-fidelity uncertainty propagation using polynomial chaos and Gaussian process modeling