Divide and conquer: An incremental sparsity promoting compressive sampling approach for polynomial chaos expansions

Computer Methods in Applied Mechanics and Engineering ◽

10.1016/j.cma.2017.01.039 ◽

2017 ◽

Vol 318 ◽

pp. 937-956 ◽

Cited By ~ 6

Author(s):

Negin Alemazkoor ◽

Hadi Meidani

Keyword(s):

Polynomial Chaos ◽

Compressive Sampling ◽

Divide And Conquer ◽

Polynomial Chaos Expansions ◽

Sampling Approach

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Compressive Sampling Methods for Sparse Polynomial Chaos Expansions

Handbook of Uncertainty Quantification ◽

10.1007/978-3-319-12385-1_67 ◽

2017 ◽

pp. 827-855 ◽

Cited By ~ 1

Author(s):

Jerrad Hampton ◽

Alireza Doostan

Keyword(s):

Polynomial Chaos ◽

Sampling Methods ◽

Compressive Sampling ◽

Sparse Polynomial ◽

Polynomial Chaos Expansions

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Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies

Journal of Computational Physics ◽

10.1016/j.jcp.2014.09.019 ◽

2015 ◽

Vol 280 ◽

pp. 363-386 ◽

Cited By ~ 102

Author(s):

Jerrad Hampton ◽

Alireza Doostan

Keyword(s):

Convergence Analysis ◽

Polynomial Chaos ◽

Compressive Sampling ◽

Sampling Strategies ◽

Polynomial Chaos Expansions

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Compressive Sampling Methods for Sparse Polynomial Chaos Expansions

Handbook of Uncertainty Quantification ◽

10.1007/978-3-319-11259-6_67-1 ◽

2015 ◽

pp. 1-29 ◽

Cited By ~ 3

Author(s):

Jerrad Hampton ◽

Alireza Doostan

Keyword(s):

Polynomial Chaos ◽

Sampling Methods ◽

Compressive Sampling ◽

Sparse Polynomial ◽

Polynomial Chaos Expansions

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Sparse Approximation of Data-Driven Polynomial Chaos Expansions: An Induced Sampling Approach

Communications in Mathematical Research ◽

10.4208/cmr.2020-0010 ◽

2020 ◽

Vol 36 (2) ◽

pp. 128-153 ◽

Cited By ~ 1

Author(s):

Ling Guo

Keyword(s):

Polynomial Chaos ◽

Sparse Approximation ◽

Data Driven ◽

Polynomial Chaos Expansions ◽

Sampling 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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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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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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