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

: Janya-anurak, Chettapong
: Beyerer, Jürgen

Fulltext urn:nbn:de:0072-669407 (16 MByte PDF)
MD5 Fingerprint: 059ac80ac3e0003f2c81726b82e08dc0
Created on: 11.4.2017

Karlsruhe: KIT Scientific Publishing, 2017, XIX, 210 pp.
Zugl.: Karlsruhe, Inst. für Technologie (KIT), Diss., 2017
Karlsruher Schriften zur Anthropomatik, 31
ISBN: 978-3-7315-0642-3
Dissertation, Electronic Publication
Fraunhofer IOSB ()

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.