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Data-driven polynomial chaos expansions: A weighted least-square approximation · Study

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ArticleData-driven polynomial chaos expansions: A weighted least-square approximation

Ling Guo2019

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Articlegreen2019

Data-driven polynomial chaos expansions: A weighted least-square approximation

Ling Guo · Yongle Liu

23 citations·0/0 files stored·Journal of Computational Physics

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Probabilistic and Robust Engineering Design Mathematical functions and polynomials Model Reduction and Neural Networks

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References

2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations 2005High-Order Collocation Methods for Differential Equations with Random Inputs 2007A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data 2004Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure 2006Multi-Element Generalized Polynomial Chaos for Arbitrary Probability Measures 2008An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data 2012Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion 2011A non-adapted sparse approximation of PDEs with stochastic inputs

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Year2019

Typearticle

Published2019-01-08

VenueJournal of Computational Physics

ISSN-L0021-9991

Languageen

Citations23

Files0 public / 0

ShelfProbabilistic and Robust Engineering Design

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doi10.1016/j.jcp.2018.12.020

openalex_workW2962678232

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