The deep latent space particle filter for real-time data assimilation with uncertainty quantification
Nikolaj T. Mücke,
Sander M. Bohté,
Cornelis W. Oosterlee
Abstract:In data assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining data with a model, however, while accurately estimating uncertainty, is computationally expensive and infeasible to run in realtime for complex systems. Here, we present a novel particle filter methodology, the Deep Latent Space Particle filter or D-LSPF, that uses neural network-based surrogate models to overcome this computational challenge. The D-LSP… Show more
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