Accurate solution of Bayesian inverse uncertainty quantification problems combining reduced basis methods and reduction error models

Manzoni, A., Pagani, S. and Lassila, T. (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

Abstract

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Authors/Creators:
  • Manzoni, A.
  • Pagani, S.
  • Lassila, T.
Copyright, Publisher and Additional Information: © 2016, Society for Industrial and Applied Mathematics This is an author produced version of a paper subsequently published in SIAM/ASA Journal on Uncertainty Quantification. Uploaded in accordance with the publisher's self-archiving policy.
Dates:
  • Accepted: 16 February 2016
  • Published (online): 19 April 2016
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 07 Mar 2016 13:38
Last Modified: 04 May 2016 03:41
Published Version: http://dx.doi.org/10.1137/140995817
Status: Published
Publisher: Society for Industrial and Applied Mathematics
Refereed: Yes
Identification Number: https://doi.org/10.1137/140995817

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