StateMixNN learns particle-filter transition and proposal densities as Gaussian mixtures parameterized by neural networks, trained only on the observation likelihood, and reports improved state recovery on Lorenz 96 and Kuramoto systems.
Operat ional implementation of a hybrid ensemble /4d- Var global data assimilation system at the Met O ffice
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Learning state and proposal dynamics in state-space models using differentiable particle filters and neural networks
StateMixNN learns particle-filter transition and proposal densities as Gaussian mixtures parameterized by neural networks, trained only on the observation likelihood, and reports improved state recovery on Lorenz 96 and Kuramoto systems.