206 citations · 966 across the 31 of their papers we have counts for
35 papers
Hybrid physics-data-driven modeling for sea ice thermodynamics and transfer learning
Giovanni De Cillis, Alberto Carrassi, Julien Brajard +5
This study explores a physics-data driven hybrid approach for sea-ice column physics models, in which a machine learning (ML) component acts as a state-dependent parameterization o…
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…
Multi-Scale Data Assimilation in Turbulent Models
Francesco Fossella, Luca Biferale, Alberto Carrassi +2
We explore the potential of Data-Assimilation (DA) within the multi-scale framework of a shell model of turbulence, with a focus on the Ensemble Kalman Filter (EnKF). The central o…
Hybrid machine learning data assimilation for marine biogeochemistry
Ieuan Higgs, Ross Bannister, Jozef Skákala +2
Marine biogeochemistry models are critical for forecasting, as well as estimating ecosystem responses to climate change and human activities. Data assimilation (DA) improves these…
Ensemble Kalman filter in latent space using a variational autoencoder pair
Ivo Pasmans, Yumeng Chen, Tobias Sebastian Finn +2
Popular (ensemble) Kalman filter data assimilation (DA) approaches assume that the errors in both the a priori estimate of the state and those in the observations are Gaussian. For…
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…