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SPDE priors for uncertainty quantification of end-to-end neural data assimilation schemes
Recent advances in the deep learning community also enables to adress this problem as neural architecture embedding data assimilation variational framework.
Neural SPDE solver for uncertainty quantification in high-dimensional space-time dynamics
Recent advancements in deep learning also addressed this issue by incorporating data assimilation into neural architectures: it treats the reconstruction task as a joint learning problem involving both prior model and solver as neural networks.
Learning Neural Optimal Interpolation Models and Solvers
The reconstruction of gap-free signals from observation data is a critical challenge for numerous application domains, such as geoscience and space-based earth observation, when the available sensors or the data collection processes lead to irregularly-sampled and noisy observations.
4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry
We introduce a parametrization of the 4DVarNet scheme dedicated to the space-time interpolation of satellite altimeter data.
