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  4. Framework for analysis and identification of nonlinear distributed parameter systems using Bayesian uncertainty quantification based on generalized polynomial chaos
 
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2017
Doctoral Thesis
Titel

Framework for analysis and identification of nonlinear distributed parameter systems using Bayesian uncertainty quantification based on generalized polynomial chaos

Abstract
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria.
ThesisNote
Zugl.: Karlsruhe, Inst. für Technologie (KIT), Diss., 2017
Author(s)
Janya-anurak, Chettapong
Advisor
Beyerer, Jürgen
Verlag
KIT Scientific Publishing
Verlagsort
Karlsruhe
DOI
10.24406/publica-fhg-281463
File(s)
N-441502.pdf (16.9 MB)
Language
English
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Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
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