Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
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 analy...
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| Format: | Book Chapter |
| Published: |
KIT Scientific Publishing
2017
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| Summary: | 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. |
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| Physical Description: | 1 electronic resource (XIX, 210 p. p.) |
| ISBN: | KSP/1000066940 9783731506423 |
| Access: | Open Access |