Blended particle filters for large-dimensional chaotic dynamical systems
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Majda-2014-Blended particle fil.pdf
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Author(s)
Sapsis, Themistoklis
Majda, Andrew J.
Qi, Di
Date Issued
May 2014
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
National Academy of Sciences (U.S.)
Citation
Majda, A. J., D. Qi, and T. P. Sapsis. “Blended Particle Filters for Large-Dimensional Chaotic Dynamical Systems.” Proceedings of the National Academy of Sciences 111, no. 21 (May 13, 2014): 7511–7516. © National Academy of Sciences
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/pnas.1405675111
