Sparse Bayesian polynomial chaos approximations of elasto-plastic material models

Bojana Rosić; Hermann G. Matthies

Sparse Bayesian polynomial chaos approximations of elasto-plastic material models

Bojana Rosić^*, Hermann G. Matthies

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

84 Downloads (Pure)

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
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
Abbreviated title	COMPLAS 2017
Country/Territory	Spain
City	Barcelona
Period	5/09/17 → 7/09/17
Internet address	http://congress.cimne.com/complas2017/frontal/Series.asp

Keywords

Iterative spectral filter
Sparse Bayesian inference
Sparse polynomial chaos expansion
Spectral kalman filtering
Stochastic elastoplasticity
Uncertainty quantification

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Rosic2017sparseFinal published version, 506 KB

http://hdl.handle.net/2117/180670

Cite this

@inproceedings{b3cc78bb0369446f8874d158d8ac240b,

title = "Sparse Bayesian polynomial chaos approximations of elasto-plastic material models",

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.",

keywords = "Iterative spectral filter, Sparse Bayesian inference, Sparse polynomial chaos expansion, Spectral kalman filtering, Stochastic elastoplasticity, Uncertainty quantification",

author = "Bojana Rosi{\'c} and Matthies, {Hermann G.}",

year = "2017",

month = jan,

day = "1",

language = "English",

isbn = "978-84-946909-6-9",

pages = "256--267",

editor = "Eugenio Onate and Djordje Peric and Owen, {D. Roger J.} and Michele Chiumenti",

booktitle = "Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017",

publisher = "CIMNE",

note = "14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017, COMPLAS 2017 ; Conference date: 05-09-2017 Through 07-09-2017",

url = "http://congress.cimne.com/complas2017/frontal/Series.asp",

}

Rosić, B & Matthies, HG 2017, Sparse Bayesian polynomial chaos approximations of elasto-plastic material models. in E Onate, D Peric, DRJ Owen & M Chiumenti (eds), Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017. CIMNE, Barcelona, pp. 256-267, 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017, Barcelona, Spain, 5/09/17. <http://hdl.handle.net/2117/180670>

Sparse Bayesian polynomial chaos approximations of elasto-plastic material models. / Rosić, Bojana; Matthies, Hermann G.
Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017. ed. / Eugenio Onate; Djordje Peric; D. Roger J. Owen; Michele Chiumenti. Barcelona: CIMNE, 2017. p. 256-267.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Sparse Bayesian polynomial chaos approximations of elasto-plastic material models

AU - Rosić, Bojana

AU - Matthies, Hermann G.

N1 - Conference code: 14

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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.

AB - 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.

KW - Iterative spectral filter

KW - Sparse Bayesian inference

KW - Sparse polynomial chaos expansion

KW - Spectral kalman filtering

KW - Stochastic elastoplasticity

KW - Uncertainty quantification

UR - http://www.scopus.com/inward/record.url?scp=85045321376&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85045321376

SN - 978-84-946909-6-9

SP - 256

EP - 267

BT - Proceedings of the 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017

A2 - Onate, Eugenio

A2 - Peric, Djordje

A2 - Owen, D. Roger J.

A2 - Chiumenti, Michele

PB - CIMNE

CY - Barcelona

T2 - 14th International Conference on Computational Plasticity - Fundamentals and Applications, COMPLAS 2017

Y2 - 5 September 2017 through 7 September 2017

ER -

Sparse Bayesian polynomial chaos approximations of elasto-plastic material models

Abstract

Conference

Keywords

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