13 citations · 16 across the 2 of their papers we have counts for
4 papers
Machine Learning for the Physics of Climate
Annalisa Bracco, Julien Brajard, Henk A. Dijkstra +3
An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations…
Towards diffusion models for large-scale sea-ice modelling
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi +2
We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by d…
Parameter sensitivity analysis of a sea ice melt pond parametrisation and its emulation using neural networks
Simon Driscoll, Alberto Carrassi, Julien Brajard +3
Accurate simulation of sea ice is critical for predictions of future Arctic sea ice loss, looming climate change impacts, and more. A key feature in Arctic sea ice is the formation…
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Sibo Cheng, Cesar Quilodran-Casas, Said Ouala +14
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical a…