Nonlinear data assimilation [electronic resource]
- 作者： Leeuwen, Peter Jan van.
- 其他作者：
- 其他題名：
- Frontiers in applied dynamical systems.
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： Frontiers in applied dynamical systems: reviews and tutorials,v.2
- 主題： Simulation methods , Mathematical models , Mathematics , Dynamical Systems and Ergodic Theory. , Computational Mathematics and Numerical Analysis. , Mathematical Applications in the Physical Sciences.
- ISBN： 9783319183473 (electronic bk.) 、 9783319183466 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 摘要註: This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.
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讀者標籤：
- 系統號： 005135409 | 機讀編目格式
館藏資訊
This book contains two review articles on nonlinear data assimilation that deal with closely related topics but were written and can be read independently. Both contributions focus on so-called particle filters. The first contribution by Jan van Leeuwen focuses on the potential of proposal densities. It discusses the issues with present-day particle filters and explorers new ideas for proposal densities to solve them, converging to particle filters that work well in systems of any dimension, closing the contribution with a high-dimensional example. The second contribution by Cheng and Reich discusses a unified framework for ensemble-transform particle filters. This allows one to bridge successful ensemble Kalman filters with fully nonlinear particle filters, and allows a proper introduction of localization in particle filters, which has been lacking up to now.