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Tobias Finn
Tobias Finn
CEREA, Joint Laboratory École des Ponts ParisTech and EDF R&D, Université Paris-Est
在 enpc.fr 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
102023
Towards assimilation of wind profile observations in the atmospheric boundary layer with a sub-kilometre-scale ensemble data assimilation system
TS Finn, G Geppert, F Ament
Tellus A: Dynamic Meteorology and Oceanography 72 (1), 1-14, 2020
92020
Self-attentive ensemble transformer: Representing ensemble interactions in neural networks for earth system models
TS Finn
arXiv preprint arXiv:2106.13924, 2021
72021
Backfilling in Australian mines: a new application in underground coal operations
T Grice, T Finn, P Smith
Australian Coal Review, 1999
51999
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
42023
Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks
C Burgard, NC Jourdain, P Mathiot, RS Smith, R Schäfer, J Caillet, ...
Journal of Advances in Modeling Earth Systems 15 (12), e2023MS003829, 2023
22023
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
12024
Assessing the weather conditions for urban cyclists by spatially dense measurements with an agent‐based approach
AU Schmitt, F Burgemeister, H Dorff, T Finn, A Hansen, B Kirsch, I Lange, ...
Meteorological Applications 30 (6), e2164, 2023
12023
Multivariate state and parameter estimation with data assimilation on sea-ice models using a Maxwell-Elasto-Brittle rheology
Y Chen, P Smith, A Carrassi, I Pasmans, L Bertino, M Bocquet, TS Finn, ...
EGUsphere 2023, 1-36, 2023
12023
Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic
C Durand, TS Finn, A Farchi, M Bocquet, E Òlason
EGUsphere 2023, 1-38, 2023
12023
Assessing the weather conditions for urban cyclists by spatially dense measurements
F Ament, A Schmitt, F Burgemeister, H Dorff, T Finn, A Hansen, B Kirsch, ...
EMS2022, 2022
12022
Ensemble-based data assimilation of atmospheric boundary layer observations improves the soil moisture analysis
TS Finn, G Geppert, F Ament
Hydrology and Earth System Sciences Discussions 2021, 1-27, 2021
12021
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
2024
Multivariate state and parameter estimation with data assimilation applied to sea-ice models using a Maxwell elasto-brittle rheology
Y Chen, P Smith, A Carrassi, I Pasmans, L Bertino, M Bocquet, TS Finn, ...
The Cryosphere 18 (5), 2381-2406, 2024
2024
Generative diffusion for regional surrogate models from sea-ice simulations
TS Finn, C Durand, A Farchi, M Bocquet, P Rampal, A Carrassi
Authorea Preprints, 2024
2024
Multivariate state and parameter estimation using data assimilation in a Maxwell-Elasto-Brittle sea ice model
Y Chen, P Smith, A Carrassi, I Pasmans, L Bertino, M Bocquet, TS Finn, ...
EGU24, 2024
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
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
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
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