Constitutive model error and uncertainty quantification

Constitutive model error and uncertainty quantification This paper proposes a novel Bayesian strategy for high‐dimensional inverse problems in the context of elastostatics. Apart from parametric uncertainties, model inadequacies and, particularly, constitutive model errors, are also addressed. This is especially important in biomedical settings when the inferred material properties will be used to make decisions/diagnoses. Traditional approaches use an additional regression model (e.g., Gaussian process), added to the model output or within a submodel to account, for an underlying model error. This can violate physical constraints and becomes impractical in high dimensions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings in Applied Mathematics & Mechanics Wiley

Constitutive model error and uncertainty quantification

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Publisher
Wiley
Copyright
Copyright © 2017 Wiley Subscription Services
ISSN
1617-7061
eISSN
1617-7061
D.O.I.
10.1002/pamm.201710400
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a novel Bayesian strategy for high‐dimensional inverse problems in the context of elastostatics. Apart from parametric uncertainties, model inadequacies and, particularly, constitutive model errors, are also addressed. This is especially important in biomedical settings when the inferred material properties will be used to make decisions/diagnoses. Traditional approaches use an additional regression model (e.g., Gaussian process), added to the model output or within a submodel to account, for an underlying model error. This can violate physical constraints and becomes impractical in high dimensions.

Journal

Proceedings in Applied Mathematics & MechanicsWiley

Published: Jan 1, 2017

References

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