Janya-anurak, Chettapong <br/><br/>
Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos 

 -  KIT Scientific Publishing  2017 
 - 1 electronic resource (XIX, 210 p. p.) 
<br/><br/> Open Access 
<br/><br/> 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. 
<br/><br/><br/>Creative Commons 
<br/><br/><br/>English 
<br/><br/><label>ISBN: </label>KSP/1000066940 9783731506423 
<br/><br/><label>Standard No.: </label>10.5445/KSP/1000066940  doi 
<br/><br/><label>Subjects--Index Terms: </label>ParameterschätzungUncertainty Quantification Parameter estimation verteilt-parametrische Systeme Sensitivity Analysis generalized polynomial chaos Distributed Parameter Systems Sensitivitätsanalyse Unsicherheit Quantifizierung