Publications (32)
Ensemble Kalman Filter for non-conservative moving mesh solvers with a joint physics and mesh location update
Christian Sampson, Alberto Carrassi, Ali AydoÄdu +1
Numerical solvers using adaptive meshes can focus computational power on important regions of a model domain capturing important or unresolved physics. The adaptation can be inform…
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
Marc Bocquet, Julien Brajard, Alberto Carrassi +1
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…
Structural Origins and Real-Time Drivers of Intermittency
Alessandro Barone, Alberto Carrassi, Thomas Savary +2
In general terms, intermittency is the property for which time evolving systems alternate among two or more different regimes. Predicting the instance when the regime switch will o…
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…
Generative AI models capture realistic sea-ice evolution from days to decades
Tobias Sebastian Finn, Marc Bocquet, Pierre Rampal +4
Sea ice plays an important role in stabilising the Earth system. Yet, representing its dynamics remains a major challenge for models, as the underlying processes are scale-invarian…
Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble
Marc Bocquet, Alban Farchi, Tobias S. Finn +5
We investigate the ability to discover data assimilation (DA) schemes meant for chaotic dynamics with deep learning. The focus is on learning the analysis step of sequential DA, fr…