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Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering.
“KPConv: Flexible and Deformable Convolution for Point Clouds”, 2019
Hugues Thomas, Charles. Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette and Leonidas. Guibas · 1904
Earlier work this paper cites.
B.. Gross, N. Trask, P. Kuberry and P.. Atzberger · 1905
Earlier work this paper cites.
“Multivariable Functional Interpolation and Adaptive Networks”
D.S. Broomhead and D. Lowe · 1988
Earlier work this paper cites.
“Networks for approximation and learning”
T. Poggio and F. Girosi · 1990
Earlier work this paper cites.
“Brownian Motion and Stochastic Calculus”
Ioannis Karatzas and Steven Shreve · 1998
Earlier work this paper cites.
“Artificial neural networks for solving ordinary and partial differential equations”
I.. Lagaris, A. Likas and D.. Fotiadis · 1998
Earlier work this paper cites.
“Scattered data approximation”
Holger Wendland · 2004
Earlier work this paper cites.
“The Graph Neural Network Model”
Franco Scarselli, Marco Gori, Ah Tsoi, Markus Hagenbuchner and Gabriele Monfardini · 2009
Earlier work this paper cites.
“Spectral networks and locally connected networks on graphs”
Joan Bruna, Wojciech Zaremba, Arthur Szlam and Yann Lecun · 2014
Earlier work this paper cites.
“Discovering governing equations from data by sparse identification of nonlinear dynamical systems”, 2016, pp. 3932–3937
Steven. Brunton, Joshua. Proctor and J. Kutz · 2016
Earlier work this paper cites.
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Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodol\‘a, Jan Svoboda and Michael. Bronstein · 2016
Cited alongside, same era.
“Geometric Deep Learning: Going beyond Euclidean data”
M.. Bronstein, J. Bruna, Y. LeCun, A. Szlam and P. Vandergheynst · 2017
Cited alongside, same era.
“PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation”
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Cited alongside, same era.
“PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space”
Charles Qi, Li Yi, Hao Su and Leonidas Guibas · 2017
Cited alongside, same era.
“Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence”, 2018
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Later among the works it cites.
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M. Fey, J.. Lenssen, F. Weichert and H. Müller · 2018
Later among the works it cites.
“A virtual control meshfree coupling method for non-coincident interfaces”, 2018
Paul Kuberry, Pavel Bochev and Kara Peterson · 2018
Later among the works it cites.
“PDE-Net: Learning PDEs from Data”
Zichao Long, Yiping Lu, Xianzhong Ma and Bin Dong · 2018
Later among the works it cites.
“Nonlinear integro-differential operator regression with neural networks”
Ravi. Patel and Olivier Desjardins · 2018
Later among the works it cites.
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“Data-driven discovery of partial differential equations”, 2017
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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov and Alexander Smola · 2017
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P.. Atzberger · 2018
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Maziar Raissi and George Karniadakis · 2018
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“Learning data-driven discretizations for partial differential equations”
Yohai Bar-Sinai, Stephan Hoyer, Jason Hickey and Michael. Brenner · 2019
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“A conservative, consistent, and scalable meshfree mimetic method”
Nathaniel Trask, Pavel Bochev and Mauro Perego · 2019
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