A Bayesian Approach for Uncertainty Quantification in Elliptic Cauchy Problem
In: Virtual Design and Validation ISBN: 9783030381554 Virtual design and validation Virtual design and validation, Springer, pp.293-308, 2020, 978-3-030-38156-1. ⟨10.1007/978-3-030-38156-1_15⟩; (2020)
Online
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Zugriff:
International audience; We study the Cauchy problem in the framework of static linear elasticity and its resolution via the Steklov-Poincaré approach. In the linear Gaussian framework, the straightforward application of Bayes theory leads to formulas allowing to deduce the uncertainty on the identified field from the noise level. We use a truncated Ritz decomposition of the Steklov-Poincaré operator, which reduces the number of degrees of freedom and significantly lowers the computational cost.
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A Bayesian Approach for Uncertainty Quantification in Elliptic Cauchy Problem
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| Autor/in / Beteiligte Person: | Mohamed Larbi Kadri ; Gosselet, Pierre ; Ferrier, Renaud ; Matthies, Hermann G. ; Laboratoire de Mécanique et Technologie (LMT) ; École normale supérieure - Cachan (ENS Cachan)-Centre National de la Recherche Scientifique (CNRS) ; Laboratoire de Modélisation Mathématique et Numérique dans les Sciences de l'Ingénieur [Tunis] (LR-LAMSIN-ENIT) ; Ecole Nationale d'Ingénieurs de Tunis (ENIT) ; Université de Tunis El Manar (UTM)-Université de Tunis El Manar (UTM) ; Institut für Wissenschaftliches Rechnen (WiRe) ; Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig] ; ViVaCE—IRTG 1627 program |
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| Quelle: | Virtual Design and Validation ISBN: 9783030381554 Virtual design and validation Virtual design and validation, Springer, pp.293-308, 2020, 978-3-030-38156-1. ⟨10.1007/978-3-030-38156-1_15⟩; (2020) |
| Veröffentlichung: | Springer International Publishing, 2020 |
| Medientyp: | unknown |
| ISBN: | 978-3-030-38155-4 (print) ; 978-3-030-38156-1 (print) |
| DOI: | 10.1007/978-3-030-38156-1_15 |
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