Data-driven model reduction for the Bayesian solution of inverse problems

One of the major challenges in the Bayesian solution of inverse problems governed by partial differential equations (PDEs) is the computational cost of repeatedly evaluating numerical PDE models, as required by Markov chain Monte Carlo (MCMC) methods for posterior sampling. This paper proposes a dat...

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Bibliographic Details
Main Authors: Cui, Tiangang, Marzouk, Youssef M., Willcox, Karen E.
Other Authors: Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Format: Article
Language:en_US
Published: Wiley Blackwell 2015
Online Access:http://hdl.handle.net/1721.1/96976
https://orcid.org/0000-0002-4840-8545
https://orcid.org/0000-0001-8242-3290
https://orcid.org/0000-0003-2156-9338