Abstract
In this paper we studied the uncertainty quantification in a functional approximation form of elastoplastic models parameterised by material uncertainties. The problem of estimating the polynomial chaos coefficients is recast in a linear regression form by taking into consideration the possible sparsity of the solution. Departing from the classical optimisation point of view, we take a slightly different path by solving the problem in a Bayesian manner with the help of new spectral based sparse Kalman filter algorithms.
Original language | English |
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Title of host publication | Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017 |
Editors | Eugenio Onate, Djordje Peric, D. Roger J. Owen, Michele Chiumenti |
Place of Publication | Barcelona |
Publisher | CIMNE |
Pages | 256-267 |
Number of pages | 12 |
ISBN (Print) | 978-84-946909-6-9 |
Publication status | Published - 1 Jan 2017 |
Externally published | Yes |
Event | 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017 - Barcelona, Spain Duration: 5 Sept 2017 → 7 Sept 2017 Conference number: 14 http://congress.cimne.com/complas2017/frontal/Series.asp |
Conference
Conference | 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017 |
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Abbreviated title | COMPLAS 2017 |
Country/Territory | Spain |
City | Barcelona |
Period | 5/09/17 → 7/09/17 |
Internet address |
Keywords
- Iterative spectral filter
- Sparse Bayesian inference
- Sparse polynomial chaos expansion
- Spectral kalman filtering
- Stochastic elastoplasticity
- Uncertainty quantification