A model-data weak formulation for simultaneous estimation of state and model bias
We introduce a Petrov–Galerkin regularized saddle approximation which incorporates a “model” (partial differential equation) and “data” (M experimental observations) to yield estimates for both state and model bias. We provide an a priori theory that identifies two distinct contributions to the redu...
| Main Authors: | , , |
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| 其他作者: | |
| 格式: | 文件 |
| 语言: | en_US |
| 出版: |
Elsevier
2016
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| 在线阅读: | http://hdl.handle.net/1721.1/103882 https://orcid.org/0000-0001-7882-2483 https://orcid.org/0000-0002-8323-9054 https://orcid.org/0000-0002-2631-6463 |