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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model., , , and . J. Comput. Sci., (2020)Learning 4DVAR Inversion Directly from Observations., , , and . ICCS (4), volume 14076 of Lecture Notes in Computer Science, page 414-421. Springer, (2023)Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review., , , , , , , , , and 7 other author(s). IEEE CAA J. Autom. Sinica, 10 (6): 1361-1387 (June 2023)Analysis of the Senegalo-Mauritanian upwelling by processing satellite remote sensing observations with topological maps., , , , , and . IJCNN, page 2826-2832. IEEE Computer Society, (2009)Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting., , , , and . CoRR, (2020)Validation of model simulations with respect to in situ observations by the use of probabilistic estimations., , , and . IJCNN, page 3015-3019. IEEE, (2008)Towards diffusion models for large-scale sea-ice modelling., , , , and . CoRR, (2024)Learning Dynamical Systems from Partial Observations, , , , and . (2019)cite arxiv:1902.11136.Machine Learning-Based Improvement of Aerosol Optical Depth from CHIMERE Simulations Using MODIS Satellite Observations., , , , , , and . Remote. Sens., 15 (6): 1510 (March 2023)Inverse Method for the Retrieval of Ocean Vertical Profiles using Self Organizing Maps and Hidden Markov Models - Application on Ocean Colour Satellite Image Inversion., , , , and . IJCCI (NCTA), page 316-321. SciTePress, (2011)