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Simulating complex dynamics like fluids with traditional simulators is computationally challenging.
Flip: A method for adaptively zoned, particle-in-cell calculations of fluid flows in two dimensions
J.U. Brackbill and H. M. Ruppel · 1986
Earlier work this paper cites.
Topics in optimal transportation
C. Villani · 2003
Earlier work this paper cites.
Animating sand as a fluid
Y. Zhu and R. Bridson · 2005
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
M. Cuturi · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
M. Abadi et al · 2016
Earlier work this paper cites.
J. Ba, J. Kiros, and G. E. Hinton · 2016
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
P. W. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, et al · 2016
Earlier work this paper cites.
Sonnet, 2017
DeepMind · 2017
Earlier work this paper cites.
Pot python optimal transport library, 2017
R. Flamary and N. Courty · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
Cited alongside, same era.
Graph nets library, 2018
DeepMind · 2018
Cited alongside, same era.
Deep learning to represent subgrid processes in climate models
S. Rasp, M. Pritchard, and P. Gentine · 2018
Cited alongside, same era.
Graph networks as learnable physics engines for inference and control
Learning to predict the cosmological structure formation
S. He, Y. Li, Y. Feng, S. Ho, S. Ravanbakhsh, W. Chen, and B. Póczos · 2019
Later among the works it cites.
Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Y. Li, J. Wu, R. Tedrake, J. B. Tenenbaum, and A. Torralba · 2019
Later among the works it cites.
Gnnexplainer: Generating explanations for graph neural networks
R. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec · 2019
Later among the works it cites.
Discovering symbolic models from deep learning with inductive biases
M. Cranmer, A. Sanchez-Gonzalez, P. W. Battaglia, R. Xu, K. Cranmer, D. Spergel, and S. Ho · 2020
Later among the works it cites.
Learning to control pdes with differentiable physics
P. Holl, V. Koltun, and N. Thuerey · 2020
Later among the works it cites.
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A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, Merel J., M. Riedmiller, R. Hadsell, and P. W. Battaglia · 2018
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Deep learning methods for reynolds-averaged navier–stokes simulations of airfoil flows
N. Thuerey, K. Weissenow, H. Mehrotra, and N. Mainali · 2018
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Learning symbolic physics with graph networks
M. Cranmer, R. Xu, P. Battaglia, and S. Ho · 2019
Cited alongside, same era.
Learning to simulate complex physics with graph networks
A. Sanchez-Gonzalez, J. Godwin, T. Pfaff, R. Ying, J. Leskovec, and Battaglia P. W · 2020
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Graph neural networks in particle physics
J. Shlomi, P. W. Battaglia, and J. Vlimant · 2020
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Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
K. Um, Y. Fei, R. Brand, P. Holl, and N. Thuerey · 2020
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