Blended particle filters for large-dimensional chaotic dynamical systems
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
National Academy of Sciences (U.S.)
2014
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| Online Access: | http://hdl.handle.net/1721.1/91955 https://orcid.org/0000-0003-0302-0691 |
| Summary: | 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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