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01773 am a22002653u 4500 |
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103882 |
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|a Yano, Masayuki
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|a Massachusetts Institute of Technology. Department of Mechanical Engineering
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|a Yano, Masayuki
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|a Penn, James Douglass
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|a Patera, Anthony T.
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|a Penn, James Douglass
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|a Patera, Anthony T.
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|a A model-data weak formulation for simultaneous estimation of state and model bias
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|a A model-data weak formulation for simultaneous estimation of state and model bias Estimation de la variable dʼétat et du biais de modèle par une formulation faible incorporant les données
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|b Elsevier,
|c 2016-08-10T17:30:56Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/103882
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|a 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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|a United States. Air Force Office of Scientific Research (OSD/AFOSR/MURI Grant FA9550-09-1-0613)
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|a United States. Office of Naval Research (ONR Grant N00014-11-0713)
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|a SUTD-MIT International Design Centre (IDC)
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|a en_US
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|a Article
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|t Comptes Rendus Mathematique
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