13 citations as recorded by crossref.

1. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization M. Bocquet et al.
2. Computing Covariant Lyapunov Vectors in Hilbert spaces F. Noethen
3. Concept transfer of synaptic diversity from biological to artificial neural networks M. Hofmann et al.
4. Effective models and predictability of chaotic multiscale systems via machine learning F. Borra et al.
5. Mechanics and thermodynamics of a new minimal model of the atmosphere G. Vissio & V. Lucarini
6. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models M. Bocquet et al.
7. Hamiltonian Lorenz-like models F. Fedele et al.
8. Analysis of a bistable climate toy model with physics-based machine learning methods M. Gelbrecht et al.
9. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al.
10. Detecting time-irreversibility in multiscale systems: Correlation and response functions in the Lorenz96 model N. Cocciaglia & D. Lucente
11. Learning subgrid-scale models with neural ordinary differential equations S. Kang & E. Constantinescu
12. Heterogeneity of the attractor of the Lorenz ’96 model: Lyapunov analysis, unstable periodic orbits, and shadowing properties C. Maiocchi et al.
13. Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges B. Alizadeh et al.