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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, , and . Nonlinear Processes in Geophysics, 27 (3): 373-389 (2019)Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow., , , and . CoRR, (2022)Closed-form discovery of structural errors in models of chaotic systems by integrating Bayesian sparse regression and data assimilation., , and . CoRR, (2021)Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM., , , and . CoRR, (2019)Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence., , , and . CI, page 106-112. ACM, (2020)Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems., , , and . J. Comput. Phys., (March 2023)A data-driven, physics-informed framework for forecasting the spatiotemporal evolution of chaotic dynamics with nonlinearities modeled as exogenous forcings., and . J. Comput. Phys., (2021)Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence., , , , and . CoRR, (2023)Quantifying the eddy-jet feedback strength of the annular mode in an idealized GCM and reanalysis data, , and . (Aug 14, 2016)Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning., , , and . J. Comput. Phys., (2022)