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Found 4 papers, 0 papers with code
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
The focus is on learning the analysis step of sequential DA, from state trajectories and their observations, using a simple residual convolutional neural network, while assuming the dynamics to be known.
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.
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.
