Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models

Manzoni, A, Pagani, S and Lassila, T orcid.org/0000-0001-8947-1447 (2016) Accurate Solution of Bayesian Inverse Uncertainty Quantification Problems Combining Reduced Basis Methods and Reduction Error Models. SIAM/ASA Journal on Uncertainty Quantification, 4 (1). pp. 380-412. ISSN 2166-2525

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Item Type: Article
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© 2016, Society for Industrial and Applied Mathematics. Reproduced in accordance with the publisher's self-archiving policy.

Keywords: inverse problems; Bayesian inference; reduced order models; nuisance parameters; approximation error model; partial differential equations
Dates:
  • Published: 19 April 2016
  • Accepted: 16 February 2016
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 07 Mar 2019 13:38
Last Modified: 07 Mar 2019 13:38
Status: Published
Publisher: SIAM
Identification Number: 10.1137/140995817
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