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Online learning of both state and dynamics using ensemble Kalman filters
[article]
2020
arXiv
pre-print
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. However, these approaches require to consider long time series of observational data, meant to be assimilated all together. This paper investigates the possibility to
arXiv:2006.03859v1
fatcat:zplaz2lokjgitbbj3ogv2ylb4i