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ABSTRACT
Kalman Filter Dynamic Mode Decomposition for Data Assimilation
Tohoku University, Aoba 6-6-01, Aramaki, Aoba, Sendai, 980-8579, Japan.
Corresponding Author: Taku Nonomura. Email: nonomura@aero.mech.tohoku.ac.jp.
The International Conference on Computational & Experimental Engineering and Sciences 2019, 21(4), 73-74. https://doi.org/10.32604/icces.2019.05266
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In this presentation, a family of Kalman filter dynamic mode decomposition, which consists of algorithms of the linear Kalman filter DMD method which identify the linear system and the extended Kalman filter DMD method which simultaneously identify the system and estimates state variable, is introduced. Then, the application of the extended Kalman filter DMD to data assimilation is discussed.Keywords
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APA Style
Nonomura, T. (2019). Kalman filter dynamic mode decomposition for data assimilation. The International Conference on Computational & Experimental Engineering and Sciences, 21(4), 73-74. https://doi.org/10.32604/icces.2019.05266
Vancouver Style
Nonomura T. Kalman filter dynamic mode decomposition for data assimilation. Int Conf Comput Exp Eng Sciences . 2019;21(4):73-74 https://doi.org/10.32604/icces.2019.05266
IEEE Style
T. Nonomura, “Kalman Filter Dynamic Mode Decomposition for Data Assimilation,” Int. Conf. Comput. Exp. Eng. Sciences , vol. 21, no. 4, pp. 73-74, 2019. https://doi.org/10.32604/icces.2019.05266
Copyright © 2019 The Author(s). Published by Tech Science Press.This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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