Fetching the paper…
Reading the bibliography…
Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations.
A note on the generation of random normal deviates
George EP Box · 1958
Earlier work this paper cites.
Least squares quantization in PCM
Stuart Lloyd · 1982
Earlier work this paper cites.
A fast and high quality multilevel scheme for partitioning irregular graphs
George Karypis and Vipin Kumar · 1998
Earlier work this paper cites.
Domain decomposition methods for partial differential equations
Alfio Quarteroni and Alberto Valli · 1999
Earlier work this paper cites.
A restricted additive Schwarz preconditioner for general sparse linear systems
Xiao-Chuan Cai and Marcus Sarkis · 1999
Earlier work this paper cites.
Optimized Schwarz methods
M.J. Gander, L. Halpern, and F. Nataf · 2000
Earlier work this paper cites.
Introduction to algorithms (Second edition)
Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, and Clifford Stein · 2001
Earlier work this paper cites.
Domain decomposition methods—algorithms and theory , volume 34 of Springer Series in Computational Mathematics
Andrea Toselli and Olof Widlund · 2005
Earlier work this paper cites.
Optimized multiplicative, additive, and restricted additive Schwarz preconditioning
Amik St-Cyr, Martin J Gander, and Stephen J Thomas · 2007
Earlier work this paper cites.
Algebraic multigrid for discrete differential forms
William N Bell · 2008
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
Earlier work this paper cites.
Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2010
Earlier work this paper cites.
Optimal interface conditions for an arbitrary decomposition into subdomains
Martin J. Gander and Felix Kwok · 2011
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
An introduction to domain decomposition methods
Victorita Dolean, Pierre Jolivet, and Frédéric Nataf · 2015
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
Backpropagation-friendly eigendecomposition
Wei Wang, Zheng Dang, Yinlin Hu, Pascal Fua, and Mathieu Salzmann · 2019
Later among the works it cites.
Black-box learning of multigrid parameters
Alexandr Katrutsa, Talgat Daulbaev, and Ivan Oseledets · 2019
Later among the works it cites.
What graph neural networks cannot learn: depth vs width
Andreas Loukas · 2019
Later among the works it cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Later among the works it cites.
Pairnorm: Tackling oversmoothing in GNNs
Lingxiao Zhao and Leman Akoglu · 2019
Later among the works it cites.
Learning algebraic multigrid using graph neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Asynchronous optimized Schwarz methods with and without overlap
Frédéric Magoulès, Daniel B. Szyld, and Cédric Venet · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
Cited alongside, same era.
Topology adaptive graph convolutional networks
Jian Du, Shanghang Zhang, Guanhang Wu, José MF Moura, and Soummya Kar · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Cited alongside, same era.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
Cited alongside, same era.
Learning to optimize multigrid PDE solvers
Daniel Greenfeld, Meirav Galun, Ronen Basri, Irad Yavneh, and Ron Kimmel · 2019
Cited alongside, same era.
Machine learning in adaptive domain decomposition methods—predicting the geometric location of constraints
Alexander Heinlein, Axel Klawonn, Martin Lanser, and Janine Weber · 2019
Cited alongside, same era.
Ilay Luz, Meirav Galun, Haggai Maron, Ronen Basri, and Irad Yavneh · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Later among the works it cites.
Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Deli Chen, Yankai Lin, Wei Li, Peng Li, Jie Zhou, and Xu Sun · 2020
Later among the works it cites.
Optimization-based algebraic multigrid coarsening using reinforcement learning
Ali Taghibakhshi, Scott MacLachlan, Luke Olson, and Matthew West · 2021
Later among the works it cites.
Combining machine learning and domain decomposition methods for the solution of partial differential equations—a review
Alexander Heinlein, Axel Klawonn, Martin Lanser, and Janine Weber · 2021
Later among the works it cites.
A coarse space acceleration of deep-DDM
Valentin Mercier, Serge Gratton, and Pierre Boudier · 2021
Later among the works it cites.
pygmsh: A Python frontend for Gmsh, 2021
Nico Schlömer · 2021
Later among the works it cites.
PyAMG: Algebraic multigrid solvers in python
Nathan Bell, Luke N. Olson, and Jacob Schroder · 2022
Closest in time.