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...
| Những tác giả chính: | , , |
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| Tác giả khác: | |
| Định dạng: | Bài viết |
| Ngôn ngữ: | en_US |
| Được phát hành: |
Elsevier
2016
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| Truy cập trực tuyến: | 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 |
| Tóm tắt: | 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 reduction in the error in state as a function of the number of observations, M: the stability constant increases with M; the model-bias best-fit error decreases with M. We present results for a synthetic Helmholtz problem and an actual acoustics system. |
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