Online learning of both state and dynamics using ensemble Kalman filters

6 Jun 2020Marc BocquetAlban FarchiQuentin Malartic

The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning. To deal with partial and noisy observations in that endeavor, machine learning representations of the surrogate model can be used within a Bayesian data assimilation framework... (read more)

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