36 citations as recorded by crossref.

1. Surrogate modeling for the climate sciences dynamics with machine learning and data assimilation M. Bocquet https://doi.org/10.3389/fams.2023.1133226
2. Online learning of both state and dynamics using ensemble Kalman filters M. Bocquet et al. https://doi.org/10.3934/fods.2020015
3. A comparative analysis of dynamics in the generalized Lorenz model with feedforward neural network validation A. Alzahrani et al. https://doi.org/10.3934/math.2026633
4. Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1.0) S. Rasp https://doi.org/10.5194/gmd-13-2185-2020
5. Potential for equation discovery with AI in the climate sciences C. Huntingford et al. https://doi.org/10.5194/esd-16-475-2025
6. DEEP LEARNING OF CHAOTIC SYSTEMS FROM PARTIALLY-OBSERVED DATA V. Churchill & D. Xiu https://doi.org/10.1615/JMachLearnModelComput.2022045602
7. Enhancing African market predictions: Integrating quantum computing with Echo State Networks S. Seddik et al. https://doi.org/10.1016/j.sciaf.2024.e02299
8. Can Machines Learn How Clouds Work? The Epistemic Implications of Machine Learning Methods in Climate Science S. Kawamleh https://doi.org/10.1086/714877
9. A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models N. Agarwal et al. https://doi.org/10.1029/2021MS002537
10. Using machine learning to correct model error in data assimilation and forecast applications A. Farchi et al. https://doi.org/10.1002/qj.4116
11. Graph Neural Network for Colliding Particles with an Application to Sea Ice Floe Modeling R. Zhu https://doi.org/10.1007/s13369-026-11188-z
12. A Toy Model to Investigate Stability of AI‐Based Dynamical Systems B. Balogh et al. https://doi.org/10.1029/2020GL092133
13. Data-driven discovery of nonlinear dynamics in complex systems through phase-space informed neural ordinary differential equations D. Garrido González et al. https://doi.org/10.1063/5.0270212
14. Generalization properties of feed-forward neural networks trained on Lorenz systems S. Scher & G. Messori https://doi.org/10.5194/npg-26-381-2019
15. Unsupervised Learning in Echo State Networks for Input Reconstruction T. Yamada et al. https://doi.org/10.1162/NECO.a.38
16. Advances and prospects of deep learning for medium-range extreme weather forecasting L. Olivetti & G. Messori https://doi.org/10.5194/gmd-17-2347-2024
17. Surrogate Modeling Methodology for Nonlinear Atmospheric Dynamics: From Conceptual Model to Neural Networks S. Soldatenko & Y. Angudovich https://doi.org/10.3103/S1068373925020013
18. Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network A. Chattopadhyay et al. https://doi.org/10.5194/npg-27-373-2020
19. Optimizing Echo State Networks for Enhancing Large Prediction Horizons of Chaotic Time Series A. González-Zapata et al. https://doi.org/10.3390/math10203886
20. WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting S. Rasp et al. https://doi.org/10.1029/2020MS002203
21. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning A. Chattopadhyay et al. https://doi.org/10.1029/2020MS002084
22. Uncertainty Quantification When Learning Dynamical Models and Solvers With Variational Methods N. Lafon et al. https://doi.org/10.1029/2022MS003446
23. A Neural-Network Based MPAS—Shallow Water Model and Its 4D-Var Data Assimilation System X. Tian et al. https://doi.org/10.3390/atmos14010157
24. Machine learning for weather and climate are worlds apart D. Watson-Parris https://doi.org/10.1098/rsta.2020.0098
25. How to Calibrate a Dynamical System With Neural Network Based Physics? B. Balogh et al. https://doi.org/10.1029/2022GL097872
26. Variability of echo state network prediction horizon for partially observed dynamical systems A. Mahata et al. https://doi.org/10.1103/PhysRevE.108.064209
27. A comparison of combined data assimilation and machine learning methods for offline and online model error correction A. Farchi et al. https://doi.org/10.1016/j.jocs.2021.101468
28. Hyperparameter optimization for randomized algorithms: a case study on random features O. Dunbar et al. https://doi.org/10.1007/s11222-025-10587-w
29. Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework A. Farchi et al. https://doi.org/10.1029/2022MS003474
30. Deep Emulators for Differentiation, Forecasting, and Parametrization in Earth Science Simulators M. Nonnenmacher & D. Greenberg https://doi.org/10.1029/2021MS002554
31. Precipitation forecasting: from geophysical aspects to machine learning applications E. Oliveira et al. https://doi.org/10.3389/fclim.2023.1250201
32. Representation learning with unconditional denoising diffusion models for dynamical systems T. Finn et al. https://doi.org/10.5194/npg-31-409-2024
33. Dynamical analysis, geometric control and digital hardware implementation of a complex-valued laser system with a locally active memristor Y. Li et al. https://doi.org/10.1088/1674-1056/acd68b
34. Predictability assessment of cold–wet–windy pan-Atlantic extremes M. Krouma & G. Messori https://doi.org/10.1016/j.wace.2026.100903
35. Designing the futuristic dielectric elastomer minimum energy structures using artificial neural networks (ANN) B. Anupam et al. https://doi.org/10.1016/j.euromechsol.2026.106034
36. A sensitivity analysis of a regression model of ocean temperature R. Furner et al. https://doi.org/10.1017/eds.2022.10