| Deep learning subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell elasto-brittle rheology TS Finn, C Durand, A Farchi, M Bocquet, Y Chen, A Carrassi, ... The Cryosphere 17 (7), 2965-2991, 2023 | 15 | 2023 |
| Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic C Durand, TS Finn, A Farchi, M Bocquet, G Boutin, E Ólason The Cryosphere 18 (4), 1791-1815, 2024 | 8 | 2024 |
| Towards diffusion models for large-scale sea-ice modelling TS Finn, C Durand, A Farchi, M Bocquet, J Brajard arXiv preprint arXiv:2406.18417, 2024 | 4 | 2024 |
| Representation learning with unconditional denoising diffusion models for dynamical systems TS Finn, L Disson, A Farchi, M Bocquet, C Durand EGUsphere 2023, 1-39, 2023 | 4 | 2023 |
| Generative diffusion for regional surrogate models from sea‐ice simulations TS Finn, C Durand, A Farchi, M Bocquet, P Rampal, A Carrassi Journal of Advances in Modeling Earth Systems 16 (10), e2024MS004395, 2024 | 1 | 2024 |
| Representation learning with unconditional denoising diffusion models for dynamical systems TS Finn, L Disson, A Farchi, M Bocquet, C Durand Nonlinear Processes in Geophysics 31 (3), 409-431, 2024 | 1 | 2024 |
| Accurate deep learning-based filtering for chaotic dynamics by identifying instabilities without an ensemble M Bocquet, A Farchi, TS Finn, C Durand, S Cheng, Y Chen, I Pasmans, ... Chaos: An Interdisciplinary Journal of Nonlinear Science 34 (9), 2024 | 1 | 2024 |
| A data-driven sea-ice model with generative deep learning TS Finn, C Durand, F Porro, A Farchi, M Bocquet, Y Chen, A Carrassi EGU24, 2024 | | 2024 |
| Deep learning for surrogate modelling of neXtSIM C Durand, T Finn, A Farchi, M Bocquet, E Olason EGU General Assembly Conference Abstracts, EGU-12810, 2023 | | 2023 |
| Deep reinforcement learning of model error corrections T Finn, C Durand, A Farchi, M Bocquet XXVIII General Assembly of the International Union of Geodesy and Geophysics …, 2023 | | 2023 |
| Bayesian online algorithms for learning data-driven models of chaotic dynamics M Bocquet, A Farchi, Q Malartic, T Finn, C Durand, M Bonavita, ... XXVIII General Assembly of the International Union of Geodesy and Geophysics …, 2023 | | 2023 |
| Hybrid modelling with deep learning for improved sea-ice forecasting T Finn, C Durand, A Farchi, M Bocquet, Y Chen, A Carassi, V Dansereau, ... XXVIII General Assembly of the International Union of Geodesy and Geophysics …, 2023 | | 2023 |
| Learning and screening of neural networks architectures for sub-grid-scale parametrizations of sea-ice dynamics from idealised twin experiments T Finn, C Durand, A Farchi, M Bocquet, Y Chen, A Carrassi, V Dansereau EGU General Assembly Conference Abstracts, EGU22-5910, 2022 | | 2022 |
| Deep learning of subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell-Elasto-Brittle TS Finn, C Durand, A Farchi, M Bocquet, Y Chen, A Carrassi, ... | | |
| Combining data assimilation and machine learning to build data-driven models of chaotic dynamics DA-based ML & ML-based DA M Bocquet, Q Malartic, A Farchi, M Bonavita, P Laloyaux, M Chrust, T Finn, ... | | |
| Bayesian offline and online algorithms for learning data-driven models of chaotic dynamics... with applications M Bocquet, A Farchi, Q Malartic, M Bonavita, P Laloyaux, M Chrust, T Finn, ... | | |
| Beyond one iteration of machine learning and data assimilation steps for learning meteorological models? M Bocquet, A Farchi, Q Malartic, M Bonavita, P Laloyaux, M Chrust, T Finn, ... | | |
