Nonlinear Model Reduction for Uncertainty Quantification in Large-Scale Inverse Problems
We present a model reduction approach to the solution of large-scale statistical inverse problems in a Bayesian inference setting. A key to the model reduction is an efficient representation of the non-linear terms in the reduced model. To achieve this, we present a formulation that employs masked p...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
John Wiley & Sons, Inc.
2011
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| Online Access: | http://hdl.handle.net/1721.1/61711 https://orcid.org/0000-0003-2156-9338 |