Distributional uncertainty analysis using power series and polynomial chaos expansions

Z.K. Nagy; R.D. Braatz

doi:10.1016/j.jprocont.2006.10.008

Distributional uncertainty analysis using power series and polynomial chaos expansions

Journal of Process Control ◽

10.1016/j.jprocont.2006.10.008 ◽

2007 ◽

Vol 17 (3) ◽

pp. 229-240 ◽

Cited By ~ 133

Author(s):

Z.K. Nagy ◽

R.D. Braatz

Keyword(s):

Power Series ◽

Uncertainty Analysis ◽

Polynomial Chaos ◽

Polynomial Chaos Expansions

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DISTRIBUTIONAL UNCERTAINTY ANALYSIS OF A BATCH CRYSTALLIZATION PROCESS USING POWER SERIES AND POLYNOMIAL CHAOS EXPANSIONS

IFAC Proceedings Volumes ◽

10.3182/20060402-4-br-2902.00655 ◽

2006 ◽

Vol 39 (2) ◽

pp. 655-660 ◽

Cited By ~ 1

Author(s):

Z.K. Nagy ◽

R.D. Braatz

Keyword(s):

Power Series ◽

Uncertainty Analysis ◽

Crystallization Process ◽

Polynomial Chaos ◽

Batch Crystallization ◽

Polynomial Chaos Expansions

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Distributional uncertainty analysis using polynomial chaos expansions

2010 IEEE International Symposium on Computer-Aided Control System Design ◽

10.1109/cacsd.2010.5612662 ◽

2010 ◽

Cited By ~ 7

Author(s):

Zoltan K. Nagy ◽

Richard D. Braatz

Keyword(s):

Uncertainty Analysis ◽

Polynomial Chaos ◽

Polynomial Chaos Expansions

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A new surrogate modeling technique combining Kriging and polynomial chaos expansions – Application to uncertainty analysis in computational dosimetry

Journal of Computational Physics ◽

10.1016/j.jcp.2015.01.034 ◽

2015 ◽

Vol 286 ◽

pp. 103-117 ◽

Cited By ~ 105

Author(s):

Pierric Kersaudy ◽

Bruno Sudret ◽

Nadège Varsier ◽

Odile Picon ◽

Joe Wiart

Keyword(s):

Uncertainty Analysis ◽

Polynomial Chaos ◽

Surrogate Modeling ◽

Modeling Technique ◽

Polynomial Chaos Expansions

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Development of a Sparse Polynomial Chaos Expansions Method for Parameter Uncertainty Analysis

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/435/1/012011 ◽

2020 ◽

Vol 435 ◽

pp. 012011

Author(s):

C X Wang ◽

J Liu ◽

Y P Li ◽

J Zhao ◽

X M Kong

Keyword(s):

Uncertainty Analysis ◽

Parameter Uncertainty ◽

Polynomial Chaos ◽

Sparse Polynomial ◽

Polynomial Chaos Expansions ◽

Parameter Uncertainty Analysis

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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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