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Data-driven prediction and physics-agnostic machine-learning methods have attracted increased interest in recent years achieving forecast horizons going well beyond those to be expected for chaotic dynamical systems.
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Challenges and design choices for global weather and climate models based on machine learning
P. D. Dueben and P. Bauer · 2018
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J. Pathak, B. Hunt, M. Girvan, Z. Lu, and E. Ott · 2018
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D. S. Wilks · 2006
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Ensemble forecasting
M. Leutbecher and T. Palmer · 2007
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Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2008
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A. Rahimi and B. Recht · 2008
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S. Herrera, J. Fernández, M. Rodriguez, and J. Gutiérrez · 2010
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H. M. Arnold, I. M. Moroz, and T. N. Palmer · 2011
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M. Raissi, P. Perdikaris, and G. Karniadakis · 2018
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Deep learning to represent subgrid processes in climate models
S. Rasp, M. S. Pritchard, and P. Gentine · 2018
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Neural networks as interacting particle systems: Asymptotic convexity of the loss landscape and universal scaling of the approximation error, 2018
G. M. Rotskoff and E. Vanden-Eijnden · 2018
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Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
M. Bocquet, J. Brajard, A. Carrassi, and L. Bertino · 2019
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Applications of deep learning to ocean data inference and subgrid parameterization
T. Bolton and L. Zanna · 2019
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A new efficient parameter estimation algorithm for high-dimensional complex nonlinear turbulent dynamical systems with partial observations
N. Chen and A. J. Majda · 2019
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A priori estimates of the population risk for two-layer neural networks
W. E, C. Ma, and L. Wu · 2019
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Lyapunov exponents of the Kuramoto–Sivashinsky PDE
R. Edson, J. Bunder, T. Mattner, and A. Roberts · 2019
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Stochastically perturbed bred vectors in multi-scale systems
B. Giggins and G. A. Gottwald · 2019
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Uniformly accurate machine learning-based hydrodynamic models for kinetic equations
J. Han, C. Ma, Z. Ma, and W. E · 2019
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EM-like learning chaotic dynamics from noisy and partial observations, 2019
D. Nguyen, S. Ouala, L. Drumetz, and R. Fablet · 2019
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On the approximation capabilities of ReLU neural networks and random ReLU features, 2019
Y. Sun, A. Gilbert, and A. Tewari · 2019
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Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model
J. Brajard, A. Carrassi, M. Bocquet, and L. Bertino · 2020
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