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ArticleData-driven uncertainty quantification using the arbitrary polynomial chaos expansion

Sergey Oladyshkin2012

Article2012

Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion

Sergey Oladyshkin · Wolfgang Nowak

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

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Probabilistic and Robust Engineering Design Mathematical functions and polynomials Gaussian Processes and Bayesian Inference

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References

2001Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates 1991Stochastic Finite Elements: A Spectral Approach 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations 2003Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems 2007Global sensitivity analysis using polynomial chaos expansions 1996Structural Reliability Methods 2003Modeling uncertainty in flow simulations via generalized polynomial chaos 1986Structural Reliability under Incomplete Probability Information

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2015An introduction to sensitivity assessment of simulation models 2020Reliability analysis with stratified importance sampling based on adaptive Kriging 2018Review of Polynomial Chaos-Based Methods for Uncertainty Quantification in Modern Integrated Circuits 2014Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations 2019Certain trends in uncertainty and sensitivity analysis: An overview of software tools and techniques

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Year2012

Typearticle

Published2012-06-27

VenueReliability Engineering & System Safety

ISSN-L0951-8320

Languageen

Citations451

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.ress.2012.05.002

openalex_workW2113337191

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2016SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos

2019Review of statistical model calibration and validation—from the perspective of uncertainty structures

2014Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling