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91955 |
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|a Sapsis, Themistoklis
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|a Massachusetts Institute of Technology. Department of Mechanical Engineering
|e contributor
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|a Sapsis, Themistoklis
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|a Majda, Andrew J.
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|a Qi, Di
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|a Blended particle filters for large-dimensional chaotic dynamical systems
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|b National Academy of Sciences (U.S.),
|c 2014-12-01T16:05:03Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/91955
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|a Combining large uncertain computational models with big noisy datasets is a formidable problem throughout science and engineering. These are especially difficult issues when real-time state estimation and prediction are needed such as, for example, in weather forecasting. Thus, a major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. New blended particle filters are developed in this paper. These algorithms exploit the physical structure of turbulent dynamical systems and capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of the phase space.
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|a United States. Office of Naval Research. Departmental Research Initiative (N0014-10-1-0554)
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|a en_US
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|a Article
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|t Proceedings of the National Academy of Sciences of the United States of America
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