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Combining a Fully Connected Neural Network With an Ensemble Kalman Filter to Emulate a Dynamic Model in Data Assimilation
2021
IEEE Access
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 observations. First, the hybrid model couples the original dynamic model with the surrogate model. The surrogate model is learned from model forecast
doi:10.1109/access.2021.3120482
fatcat:vlpswbn6englpngdfvz6bj5zk4