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 emulate dynamic models from sparse and noisy observa

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