14 citations as recorded by crossref.

1. Analysis of a bistable climate toy model with physics-based machine learning methods M. Gelbrecht et al. 10.1140/epjs/s11734-021-00175-0
2. Computing Covariant Lyapunov Vectors in Hilbert spaces F. Noethen 10.3934/jcd.2021014
3. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. 10.3934/fods.2020015
4. Detecting time-irreversibility in multiscale systems: Correlation and response functions in the Lorenz96 model N. Cocciaglia & D. Lucente 10.1063/5.0248658
5. Effective models and predictability of chaotic multiscale systems via machine learning F. Borra et al. 10.1103/PhysRevE.102.052203
6. Mechanics and thermodynamics of a new minimal model of the atmosphere G. Vissio & V. Lucarini 10.1140/epjp/s13360-020-00814-w
7. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models M. Bocquet et al. 10.5194/npg-26-143-2019
8. Learning subgrid-scale models with neural ordinary differential equations S. Kang & E. Constantinescu 10.1016/j.compfluid.2023.105919
9. Heterogeneity of the attractor of the Lorenz ’96 model: Lyapunov analysis, unstable periodic orbits, and shadowing properties C. Maiocchi et al. 10.1016/j.physd.2023.133970
10. Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges B. Alizadeh et al. 10.1175/JHM-D-19-0164.1
11. Hamiltonian Lorenz-like models F. Fedele et al. 10.1016/j.physd.2024.134494
12. Nested smoothing algorithms for inference and tracking of heterogeneous multi-scale state-space systems S. Pérez-Vieites et al. 10.3934/fods.2025002
13. Combining data assimilation and machine learning to infer unresolved scale parametrization J. Brajard et al. 10.1098/rsta.2020.0086
14. Using machine-learning modeling to understand macroscopic dynamics in a system of coupled maps F. Borra & M. Baldovin 10.1063/5.0036809