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Many scientific and engineering processes produce spatially unstructured data.
Y. Zhang, W. J. Sung, D. N. Mavris, 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
1903
Earlier work this paper cites.
M. Selig, UIUC airfoil data site
1996
Earlier work this paper cites.
International journal for numerical methods in fluids
M. Thomadakis, M. Leschziner, A pressure-correction method for the solution of incompressible viscous flows on unstructured grids · 1996
Earlier work this paper cites.
International journal for numerical methods in engineering
C. Geuzaine, J.-F. Remacle, Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities · 2009
Earlier work this paper cites.
ACM Transactions on Mathematical Software
A. Logg, G. N. Wells, Dolfin · 2010
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, R. P. Adams, Advances in neural information processing systems
2015
Earlier work this paper cites.
X. Guo, W. Li, F. Iorio, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
2016
Earlier work this paper cites.
Physical Review E
A. Hadjighasem, D. Karrasch, H. Teramoto, G. Haller, Spectral-clustering approach to lagrangian vortex detection · 2016
Earlier work this paper cites.
F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, M. M. Bronstein, Geometric deep learning on graphs and manifolds using mixture model cnns (2016)
2016
Earlier work this paper cites.
arXiv preprint arXiv:1609.02907
T. N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks · 2016
Earlier work this paper cites.
A. B. Farimani, J. Gomes, V. S. Pande, Deep learning the physics of transport phenomena (2017)
2017
Earlier work this paper cites.
Journal of Fluid Mechanics
W. R. Graham, C. P. Ford, H. Babinsky, An impulse-based approach to estimating forces in unsteady flow · 2017
Earlier work this paper cites.
Measurement Science and Technology
J. Rabault, J. Kolaas, A. Jensen, Performing particle image velocimetry using artificial neural networks: a proof-of-concept · 2017
Earlier work this paper cites.
E. Yilmaz, B. German, 18th AIAA/ISSMO multidisciplinary analysis and optimization conference
2017
Earlier work this paper cites.
M. TP, J. RK, An efficient deep learning technique for the navier-stokes equations: Application to unsteady wake flow dynamics (2017)
2017
Earlier work this paper cites.
Journal of Fluid Mechanics
K. L. Schlueter-Kuck, J. O. Dabiri, Coherent structure colouring: identification of coherent structures from sparse data using graph theory · 2017
Earlier work this paper cites.
Nonlinear Processes in Geophysics
K. Padberg-Gehle, C. Schneide, Network-based study of lagrangian transport and mixing · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1704.04675
J. Bastings, I. Titov, W. Aziz, D. Marcheggiani, K. Sima’an, Graph convolutional encoders for syntax-aware neural machine translation · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1704.01212
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, G. E. Dahl, Neural message passing for quantum chemistry · 2017
Cited alongside, same era.
W. L. Hamilton, R. Ying, J. Leskovec, Inductive representation learning on large graphs (2017)
2017
Cited alongside, same era.
Proceedings of the National Academy of Sciences
J. Han, A. Jentzen, W. E, Solving high-dimensional partial differential equations using deep learning · 2018
Cited alongside, same era.
R. Sharma, A. B. Farimani, J. Gomes, P. Eastman, V. Pande, Weakly-supervised deep learning of heat transport via physics informed loss (2018)
2018
Cited alongside, same era.
S. Wiewel, M. Becher, N. Thuerey, Latent-space physics: Towards learning the temporal evolution of fluid flow (2018)
2018
Cited alongside, same era.
Computational Mechanics
S. Bhatnagar, Y. Afshar, S. Pan, K. Duraisamy, S. Kaushik, Prediction of aerodynamic flow fields using convolutional neural networks · 2019
Later among the works it cites.
arXiv preprint arXiv:1909.05371
N. Trask, R. G. Patel, B. J. Gross, P. J. Atzberger, Gmls-nets: A framework for learning from unstructured data · 2019
Later among the works it cites.
L. Yao, C. Mao, Y. Luo, Proceedings of the AAAI Conference on Artificial Intelligence
2019
Later among the works it cites.
Chemistry of Materials
C. Chen, W. Ye, Y. Zuo, C. Zheng, S. P. Ong, Graph networks as a universal machine learning framework for molecules and crystals · 2019
Later among the works it cites.
F. Alet, A. K. Jeewajee, M. Bauza, A. Rodriguez, T. Lozano-Perez, L. P. Kaelbling, Graph element networks: adaptive, structured computation and memory (2019)
2019
Later among the works it cites.
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Physical Review E
M. G. Meena, A. G. Nair, K. Taira, Network community-based model reduction for vortical flows · 2018
Cited alongside, same era.
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulcehre, F. Song, A. Ballard, J. Gilmer, G. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, R. Pascanu, Relational inductive biases, deep learning, and graph networks (2018)
2018
Cited alongside, same era.
B. Yu, H. Yin, Z. Zhu, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18
2018
Cited alongside, same era.
Phys. Rev. Lett
T. Xie, J. C. Grossman, Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties · 2018
Cited alongside, same era.
M. Zhang, Y. Chen, Proceedings of the 32nd International Conference on Neural Information Processing Systems
2018
Cited alongside, same era.
arXiv preprint arXiv:1811.01287
C. Cangea, P. Veličković, N. Jovanović, T. Kipf, P. Liò, Towards sparse hierarchical graph classifiers · 2018
Cited alongside, same era.
Journal of Computational Physics
M. Raissi, P. Perdikaris, G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations · 2019
Cited alongside, same era.
M. Fey, J. E. Lenssen, Fast graph representation learning with pytorch geometric · 2019
Later among the works it cites.
arXiv preprint arXiv:1905.05178
H. Gao, S. Ji, Graph u-nets · 2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, Advances in Neural Information Processing Systems 32
2019
Later among the works it cites.
Journal of Computational Physics
K. O. Lye, S. Mishra, D. Ray, Deep learning observables in computational fluid dynamics · 2020
Closest in time.
G. Pang, M. D’Elia, M. Parks, G. E. Karniadakis, npinns: nonlocal physics-informed neural networks for a parametrized nonlocal universal laplacian operator. algorithms and applications (2020)
2020
Closest in time.
Computer Methods in Applied Mechanics and Engineering
A. D. Jagtap, E. Kharazmi, G. E. Karniadakis, Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems · 2020
Closest in time.
Journal of Fluid Mechanics
J. Kim, C. Lee, Prediction of turbulent heat transfer using convolutional neural networks · 2020
Closest in time.
arXiv preprint arXiv:2005.04271
R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, K. Taira, Probabilistic neural networks for fluid flow model-order reduction and data recovery · 2020
Closest in time.
arXiv preprint arXiv:2005.00756
M. Morimoto, K. Fukami, K. Fukagata, Experimental velocity data estimation for imperfect particle images using machine learning · 2020
Closest in time.
M. Le Provost, W. Hou, J. Eldredge, AIAA Scitech 2020 Forum
2020
Closest in time.
Journal of Computational Physics
B. Gross, N. Trask, P. Kuberry, P. Atzberger, Meshfree methods on manifolds for hydrodynamic flows on curved surfaces: a generalized moving least-squares (gmls) approach · 2020
Closest in time.
A. Sanchez-Gonzalez, J. Godwin, T. Pfaff, R. Ying, J. Leskovec, P. W. Battaglia, Learning to simulate complex physics with graph networks (2020)
2020
Closest in time.