02300nam 2200445z- 450 9910346763703321202102111-000-06694-0(CKB)4920000000100885(oapen)https://directory.doabooks.org/handle/20.500.12854/47993(oapen)doab47993(oapen)47993(EXLCZ)99492000000010088520202102d2017 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierFramework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial ChaosKIT Scientific Publishing20171 online resource (XIX, 210 p. p.)Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe3-7315-0642-4 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.Computer sciencebicsscDistributed Parameter Systemsgeneralized polynomial chaosParameter estimationParameterschätzungUncertainty QuantificationSensitivitätsanalyseSensitivity AnalysisUnsicherheit Quantifizierungverteilt-parametrische SystemeComputer scienceJanya-anurak Chettapongauth1279018BOOK9910346763703321Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos3014426UNINA