Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data

Stochastic Models of Uncertainties in Computational Mechanics ◽

10.1061/9780784412237.ch08 ◽

2012 ◽

pp. 57-88

Keyword(s):

Experimental Data ◽

High Dimension ◽

Random Fields ◽

Polynomial Chaos ◽

Random Coefficients ◽

Dimension Polynomial ◽

Non Gaussian ◽

Polynomial Chaos Expansions

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Identification of high-dimension polynomial chaos expansions with random coefficients for non-Gaussian tensor-valued random fields using partial and limited experimental data

Computer Methods in Applied Mechanics and Engineering ◽

10.1016/j.cma.2010.03.013 ◽

2010 ◽

Vol 199 (33-36) ◽

pp. 2150-2164 ◽

Cited By ~ 60

Author(s):

C. Soize

Keyword(s):

Experimental Data ◽

High Dimension ◽

Random Fields ◽

Polynomial Chaos ◽

Random Coefficients ◽

Dimension Polynomial ◽

Non Gaussian ◽

Polynomial Chaos Expansions

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A computational inverse method for identification of non-Gaussian random fields using the Bayesian approach in very high dimension

Computer Methods in Applied Mechanics and Engineering ◽

10.1016/j.cma.2011.07.005 ◽

2011 ◽

Vol 200 (45-46) ◽

pp. 3083-3099 ◽

Cited By ~ 38

Author(s):

C. Soize

Keyword(s):

High Dimension ◽

Bayesian Approach ◽

Random Fields ◽

Inverse Method ◽

Gaussian Random Fields ◽

Non Gaussian ◽

Very High ◽

The Bayesian Approach

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SURROGATE MODELING FOR STOCHASTIC DYNAMICAL SYSTEMS BY COMBINING NONLINEAR AUTOREGRESSIVE WITH EXOGENOUS INPUT MODELS AND POLYNOMIAL CHAOS EXPANSIONS

International Journal for Uncertainty Quantification ◽

10.1615/int.j.uncertaintyquantification.2016016603 ◽

2016 ◽

Vol 6 (4) ◽

pp. 313-339 ◽

Cited By ~ 20

Author(s):

Chu V. Mai ◽

Minas D. Spiridonakos ◽

Eleni N. Chatzi ◽

Bruno Sudret

Keyword(s):

Dynamical Systems ◽

Polynomial Chaos ◽

Surrogate Modeling ◽

Stochastic Dynamical Systems ◽

Polynomial Chaos Expansions ◽

Nonlinear Autoregressive

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Copula-based multiple indicator kriging for non-Gaussian random fields

Spatial Statistics ◽

10.1016/j.spasta.2021.100524 ◽

2021 ◽

pp. 100524

Author(s):

Gaurav Agarwal ◽

Ying Sun ◽

Huixia J. Wang

Keyword(s):

Random Fields ◽

Gaussian Random Fields ◽

Indicator Kriging ◽

Multiple Indicator ◽

Non Gaussian

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Extending bluff-and-fix estimates for polynomial chaos expansions

Journal of Computational Science ◽

10.1016/j.jocs.2020.101287 ◽

2021 ◽

pp. 101287

Author(s):

Laura Lyman ◽

Gianluca Iaccarino

Keyword(s):

Polynomial Chaos ◽

Polynomial Chaos Expansions

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Subsampled Gauss Quadrature Nodes for Estimating Polynomial Chaos Expansions

SIAM/ASA Journal on Uncertainty Quantification ◽

10.1137/130913511 ◽

2014 ◽

Vol 2 (1) ◽

pp. 423-443 ◽

Cited By ~ 18

Author(s):

Gary Tang ◽

Gianluca Iaccarino

Keyword(s):

Polynomial Chaos ◽

Gauss Quadrature ◽

Polynomial Chaos Expansions

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Modeling of Stochastic EMC Problems with Anisotropic Polynomial Chaos Expansions

IEEE Electromagnetic Compatibility Magazine ◽

10.1109/memc.2021.9477236 ◽

2021 ◽

Vol 10 (2) ◽

pp. 70-79

Author(s):

Theodoros Zygiridis ◽

Georgios Kommatas ◽

Aristeides Papadopoulos ◽

Nikolaos Kantartzis

Keyword(s):

Polynomial Chaos ◽

Polynomial Chaos Expansions

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A preconditioning approach for improved estimation of sparse polynomial chaos expansions

Computer Methods in Applied Mechanics and Engineering ◽

10.1016/j.cma.2018.08.005 ◽

2018 ◽

Vol 342 ◽

pp. 474-489 ◽

Cited By ~ 2

Author(s):

Negin Alemazkoor ◽

Hadi Meidani

Keyword(s):

Polynomial Chaos ◽

Sparse Polynomial ◽

Polynomial Chaos Expansions

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Itô SDE--based Generator for a Class of Non-Gaussian Vector-valued Random Fields in Uncertainty Quantification

SIAM Journal on Scientific Computing ◽

10.1137/130948586 ◽

2014 ◽

Vol 36 (6) ◽

pp. A2763-A2786 ◽

Cited By ~ 14

Author(s):

Johann Guilleminot ◽

Christian Soize

Keyword(s):

Uncertainty Quantification ◽

Random Fields ◽

Gaussian Vector ◽

Non Gaussian ◽

Vector Valued

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mumpce_py: A Python Implementation of the Method of Uncertainty Minimization Using Polynomial Chaos Expansions

Journal of Research of the National Institute of Standards and Technology ◽

10.6028/jres.122.039 ◽

2017 ◽

Vol 122 ◽

Cited By ~ 2

Author(s):

David A. Sheen

Keyword(s):

Measurement Uncertainty ◽

First Principles ◽

Polynomial Chaos ◽

Software Tool ◽

Physical Models ◽

Physical Parameters ◽

Experimental Measurements ◽

Uncertainty Minimization ◽

Polynomial Chaos Expansions ◽

Parameter Values

The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) was developed as a software tool to constrain physical models against experimental measurements. These models contain parameters that cannot be easily determined from first principles and so must be measured, and some which cannot even be easily measured. In such cases, the models are validated and tuned against a set of global experiments which may depend on the underlying physical parameters in a complex way. The measurement uncertainty will affect the uncertainty in the parameter values.

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