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In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks.
Smoothed particle hydrodynamics: theory and application to non-spherical stars
R. A. Gingold and J. J. Monaghan · 1977
Earlier work this paper cites.
Elementary fluid dynamics
D. J. Acheson · 1990
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Y. Kuriyama, K. Yano, and M. Hamaguchi · 2008
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E. F. Camacho and C. B. Alba · 2013
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Incorporating failure-to-success transitions in imitation learning for a dynamic pouring task
J. D. Langsfeld, K. N. Kaipa, R. J. Gentili, J. A. Reggia, and S. K. Gupta · 2014
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From pixels to torques: Policy learning with deep dynamical models
N. Wahlström, T. B. Schön, and M. P. Deisenroth · 2015
Earlier work this paper cites.
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Earlier work this paper cites.
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D. Kingma and J. Ba · 2015
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
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Visual closed-loop control for pouring liquids
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Feedback motion planning for liquid pouring using supervised learning
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Automatic differentiation in pytorch
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Perceiving and reasoning about liquids using fully convolutional networks
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