Bayesian updating of uncertainty in description of material properties using polynomial chaos expansion
Result description
Bayesian approach to identification of material parameters provides a mathematical framework for combining different sources of information, such as expert's knowledge about the material as well as the computation model. Further, the prior state of knowledge is updated by new information obtained from experimental measurements. Such formulation offers richer information about material than more common identification strategies based on measurements fitting, which leads to the prediction only of an average value of materials properties. Moreover, Bayesian setting enables inference from noisy and limited data, which usually render identification problems illposed. Disadvantage of Bayesian updating inheres in computationally exhaustive sampling, where evaluation of each sample involves often time-demanding simulation of numerical model. To circumvent this problem, polynomial chaos expansion can be used to create a suitable efficient surrogate of a given numerical model.
Keywords
Bayesian updatingpolynomial chaos expansionstochastic finite element method
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Bayesian updating of uncertainty in description of material properties using polynomial chaos expansion
Original language description
Bayesian approach to identification of material parameters provides a mathematical framework for combining different sources of information, such as expert's knowledge about the material as well as the computation model. Further, the prior state of knowledge is updated by new information obtained from experimental measurements. Such formulation offers richer information about material than more common identification strategies based on measurements fitting, which leads to the prediction only of an average value of materials properties. Moreover, Bayesian setting enables inference from noisy and limited data, which usually render identification problems illposed. Disadvantage of Bayesian updating inheres in computationally exhaustive sampling, where evaluation of each sample involves often time-demanding simulation of numerical model. To circumvent this problem, polynomial chaos expansion can be used to create a suitable efficient surrogate of a given numerical model.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JM - Structural engineering
OECD FORD branch
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Result continuities
Project
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of International Conference on Modelling and Simulation
ISBN
978-80-01-04574-9
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
Czech Technical University in Prague
Place of publication
Praha
Event location
Praha
Event date
Jun 22, 2010
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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Result type
D - Article in proceedings
CEP
JM - Structural engineering
Year of implementation
2010