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We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes.
Sea surface temperature estimation using the noaa 6 satellite advanced very high resolution radiometer
R. L. Bernstein · 1982
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Diffusion distance for histogram comparison
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Accounting for an imperfect model in 4d-var
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Learning Optical Flow , pages 83–97
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Coupling Dynamic Equations and Satellite Images for Modelling Ocean Surface Circulation , pages 191–205
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Statistics for Spatio-Temporal Data
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Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Deep residual learning for image recognition
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Nal Kalchbrenner, Aäron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2016
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Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness , pages 3–10
Jason J. Yu, Adam W. Harley, and Konstantinos G. Derpanis · 2016
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Data-driven discovery of partial differential equations
Samuel H. Rudy, Steven L. Brunton, Joshua L. Proctor, and J. Nathan Kutz · 2017
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Accelerating Eulerian fluid simulation with convolutional networks
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian J. Goodfellow, and Sergey Levine · 2016
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Flownet 2.0: Evolution of optical flow estimation with deep networks
Eddy Ilg, Nikolaus Mayer, Tonmoy Saikia, Margret Keuper, Alexey Dosovitskiy, and Thomas Brox · 2016
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Joost Van Amersfoort, Anitha Kannan, Marc’Aurelio Ranzato, Arthur Szlam, Du Tran, and Soumith Chintala · 2017
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