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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Wiley Blackwell
2015
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| 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 |