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Found 10 papers, 1 papers with code
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics.
Learning 4DVAR inversion directly from observations
Variational data assimilation and deep learning share many algorithmic aspects in common.
Super-resolution data assimilation
Increasing the resolution of a model can improve the performance of a data assimilation system: first because model field are in better agreement with high resolution observations, then the corrections are better sustained and, with ensemble data assimilation, the forecast error covariances are improved.
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting
The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow.
Combining data assimilation and machine learning to infer unresolved scale parametrisation
Moreover, the attractor of the system is significantly better represented by the hybrid model than by the truncated model.
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
The reconstruction from observations of high-dimensional chaotic dynamics such as geophysical flows is hampered by (i) the partial and noisy observations that can realistically be obtained, (ii) the need to learn from long time series of data, and (iii) the unstable nature of the dynamics.
Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model
The output analysis is spatially complete and is used as a training set by the neural network to update the surrogate model.
Representing ill-known parts of a numerical model using a machine learning approach
In numerical modeling of the Earth System, many processes remain unknown or ill represented (let us quote sub-grid processes, the dependence to unknown latent variables or the non-inclusion of complex dynamics in numerical models) but sometimes can be observed.
Learning Dynamical Systems from Partial Observations
We consider the problem of forecasting complex, nonlinear space-time processes when observations provide only partial information of on the system's state.
