Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation

Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation

Using neural network technology, dynamic characteristics can be learned from model output or assimilation results to train the model, which has greatly progressed recently. A data-driven data assimilation method is proposed by combining fully connected neural network with ensemble Kalman filter to e...

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Bibliographic Details
Main Authors: Manhong Fan, Yulong Bai, Lili Wang, Lin Ding
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Data assimilation
fully connected neural network
machine learning
ensemble Kalman filter
Lorenz model
Online Access:https://ieeexplore.ieee.org/document/9576076/

Internet

https://ieeexplore.ieee.org/document/9576076/

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