Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
The neXtSIM-DG dynamical core: A Framework for Higher-order Finite Element Sea Ice Modeling.
Deep learning of subgrid-scale parametrisations for short-term forecasting of sea-ice dynamics with a Maxwell-Elasto-Brittle rheology.
Supervised machine learning to estimate instabilities in chaotic systems: estimation of local Lyapunov exponents.
Novel Arctic sea ice data assimilation combining ensemble Kalman filter with a Lagrangian sea ice model.
A New Brittle Rheology and Numerical Framework for Large-Scale Sea-Ice Models.
Modelling the Arctic Wave-Affected Marginal Ice Zone: A Comparison with ICESat-2 Observations.
A Continuum Viscous-Elastic-Brittle, Finite Element DG Model for the Fracture and Drift of Sea Ice
Marginal ice zone fraction benchmarks sea ice and climate model skill (Nature Communications).