Search Results for author:
Found 9 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.
Towards diffusion models for large-scale sea-ice modelling
We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states.
Online model error correction with neural networks: application to the Integrated Forecasting System
In this article, we propose to develop a model error correction for the operational Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts using a neural network.
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.
Online model error correction with neural networks in the incremental 4D-Var framework
Data assimilation is used to estimate the system state from the observations, while machine learning computes a surrogate model of the dynamical system based on those estimated states.
State, global and local parameter estimation using local ensemble Kalman filters: applications to online machine learning of chaotic dynamics
It features both local domains and covariance localisation in order to learn the chaotic dynamics and the local forcings.
A comparison of combined data assimilation and machine learning methods for offline and online model error correction
We compare online and offline learning using the same framework with the two-scale Lorenz system, and show that with online learning, it is possible to extract all the information from sparse and noisy observations.
Using machine learning to correct model error in data assimilation and forecast applications
This yields a class of iterative methods in which, at each iteration a DA step assimilates the observations, and alternates with a ML step to learn the underlying dynamics of the DA analysis.
Online learning of both state and dynamics using ensemble Kalman filters
The reconstruction of the dynamics of an observed physical system as a surrogate model has been brought to the fore by recent advances in machine learning.
