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ArticleSensitivity study of dynamic systems using polynomial chaos

Eduardo Haro2012

OA

Articlegreen2012

Sensitivity study of dynamic systems using polynomial chaos

Eduardo Haro · Floriane Anstett-Collin · Michel Basset

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

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Probabilistic and Robust Engineering Design Model Reduction and Neural Networks Gaussian Processes and Bayesian Inference

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References

1991Stochastic Finite Elements: A Spectral Approach 2007Global sensitivity analysis using polynomial chaos expansions 1999A Quantitative Model-Independent Method for Global Sensitivity Analysis of Model Output 1996Importance measures in global sensitivity analysis of nonlinear models 2005Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models 2004Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models 2003Modeling uncertainty in flow simulations via generalized polynomial chaos 2002Sensitivity Analysis for Importance Assessment

Cited by

2016Uncertainty quantification and global sensitivity analysis of complex chemical process using a generalized polynomial chaos approach 2017Accurate Reduced Dimensional Polynomial Chaos for Efficient Uncertainty Quantification of Microwave/RF Networks 2014Dynamic system uncertainty propagation using polynomial chaos 2014Space-partition method for the variance-based sensitivity analysis: Optimal partition scheme and comparative study 2017Incomplete statistical information limits the utility of high-order polynomial chaos expansions

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Details

Year2012

Typearticle

Published2012-04-12

VenueReliability Engineering & System Safety

ISSN-L0951-8320

Languageen

Citations60

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.ress.2012.04.001

openalex_workW2083166992

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2013Global sensitivity analysis for dynamic systems with stochastic input processes

2018Reduced Dimensional Chebyshev-Polynomial Chaos Approach for Fast Mixed Epistemic-Aleatory Uncertainty Quantification of Transmission Line Networks

2017Cross-covariance based global dynamic sensitivity analysis