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...
Wedi'i Gadw mewn:
| Prif Awdur: | |
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| Fformat: | Online |
| Iaith: | Saesneg |
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KIT Scientific Publishing
2021
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| Pynciau: | |
| Mynediad Ar-lein: | 34936 |
| Tagiau: |
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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| _version_ | 1869523925554167808 |
|---|---|
| author | Janya-anurak, Chettapong |
| author_browse | Janya-anurak, Chettapong |
| author_facet | Janya-anurak, Chettapong |
| author_sort | Janya-anurak, Chettapong |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-47993 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-479932023-12-20T18:40:48Z Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos Janya-anurak, Chettapong QA75.5-76.95 ParameterschätzungUncertainty Quantification Parameter estimation verteilt-parametrische Systeme Sensitivity Analysis generalized polynomial chaos Distributed Parameter Systems Sensitivitätsanalyse Unsicherheit Quantifizierung bic Book Industry Communication::U Computing & information technology::UY Computer science 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. 2021-02-11T14:00:41Z 2021-02-11T14:00:41Z 2019-07-30 20:01:59 2017 book 34936 18636489 9783731506423 https://directory.doabooks.org/handle/20.500.12854/47993 eng Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe image/jpeg Attribution-ShareAlike 4.0 International https://www.ksp.kit.edu/9783731506423 KIT Scientific Publishing 10.5445/KSP/1000066940 10.5445/KSP/1000066940 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731506423 XIX, 210 p. open access |
| spellingShingle | QA75.5-76.95 ParameterschätzungUncertainty Quantification Parameter estimation verteilt-parametrische Systeme Sensitivity Analysis generalized polynomial chaos Distributed Parameter Systems Sensitivitätsanalyse Unsicherheit Quantifizierung bic Book Industry Communication::U Computing & information technology::UY Computer science Janya-anurak, Chettapong Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos |
| title | Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos |
| title_full | Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos |
| title_fullStr | Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos |
| title_full_unstemmed | Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos |
| title_short | Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos |
| title_sort | framework for analysis and identification of nonlinear distributed parameter systems using bayesian uncertainty quantification based on generalized polynomial chaos |
| topic | QA75.5-76.95 ParameterschätzungUncertainty Quantification Parameter estimation verteilt-parametrische Systeme Sensitivity Analysis generalized polynomial chaos Distributed Parameter Systems Sensitivitätsanalyse Unsicherheit Quantifizierung bic Book Industry Communication::U Computing & information technology::UY Computer science |
| topic_facet | QA75.5-76.95 ParameterschätzungUncertainty Quantification Parameter estimation verteilt-parametrische Systeme Sensitivity Analysis generalized polynomial chaos Distributed Parameter Systems Sensitivitätsanalyse Unsicherheit Quantifizierung bic Book Industry Communication::U Computing & information technology::UY Computer science |
| url | 34936 |
| work_keys_str_mv | AT janyaanurakchettapong frameworkforanalysisandidentificationofnonlineardistributedparametersystemsusingbayesianuncertaintyquantificationbasedongeneralizedpolynomialchaos |