MOR-Based Approach to Uncertainty Quantification in Electrokinetics With Correlated Random Material Parameters

A strategy for uncertainty quantification in electrokinetic problems with correlated random material parameters is proposed. Such approach exploits a reduced-order model and a polynomial spectral approximation of the deterministic parametric electrokinetic problem for accurately and efficiently esti...

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Veröffentlicht in:	IEEE transactions on magnetics 2017-06, Vol.53 (6), p.1-4
Hauptverfasser:	Codecasa, Lorenzo, Di Rienzo, Luca
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation Approximation algorithms Computer simulation Correlated random variables Correlation Electrokinetics electrokinetics problem Estimation Magnetism Mathematical models Monte Carlo methods Monte Carlo simulation Parameter uncertainty parametric model-order reduction (PMOR) Parametric statistics Probability density function Random variables Reduced order models Reduced order systems Spectra spectral approximation Statistics Uncertainty uncertainty quantification
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Beschreibung
Zusammenfassung:	A strategy for uncertainty quantification in electrokinetic problems with correlated random material parameters is proposed. Such approach exploits a reduced-order model and a polynomial spectral approximation of the deterministic parametric electrokinetic problem for accurately and efficiently estimating the statistics of the quantities of interest by Monte Carlo analysis.
ISSN:	0018-9464 1941-0069
DOI:	10.1109/TMAG.2017.2666845