000			01939naaaa2200337uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/47993
005			20220219222340.0
020			_aKSP/1000066940
020			_a9783731506423
024	7		_a10.5445/KSP/1000066940 _cdoi
041	0		_aEnglish
042			_adc
100	1		_aJanya-anurak, Chettapong _4auth
245	1	0	_aFramework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
260			_bKIT Scientific Publishing _c2017
300			_a1 electronic resource (XIX, 210 p. p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aIn 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.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by-sa/4.0/ _2cc _4https://creativecommons.org/licenses/by-sa/4.0/
546			_aEnglish
653			_aParameterschätzungUncertainty Quantification
653			_aParameter estimation
653			_averteilt-parametrische Systeme
653			_aSensitivity Analysis
653			_ageneralized polynomial chaos
653			_aDistributed Parameter Systems
653			_aSensitivitätsanalyse
653			_aUnsicherheit Quantifizierung
856	4	0	_awww.oapen.org _uhttps://www.ksp.kit.edu/9783731506423 _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/47993 _70 _zDOAB: description of the publication
999			_c47748 _d47748