Fetching the paper…
Reading the bibliography…
The great learning ability of deep learning models facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour.
L. S. Pontryagin, Mathematical theory of optimal processes . CRC press, 1987
1987
Earlier work this paper cites.
J. J. Monaghan, “Smoothed particle hydrodynamics,” Annual review of astronomy and astrophysics , vol. 30, no. 1, pp. 543–574, 1992
1992
Earlier work this paper cites.
S. Chen and G. D. Doolen, “Lattice boltzmann method for fluid flows,” Annual review of fluid mechanics , vol. 30, no. 1, pp. 329–364, 1998
1998
Earlier work this paper cites.
J. H. Ferziger, M. Perić, and R. L. Street, Computational methods for fluid dynamics . Springer, 2002, vol. 3
2002
Earlier work this paper cites.
O. C. Zienkiewicz, R. L. Taylor, and J. Z. Zhu, The finite element method: its basis and fundamentals . Elsevier, 2005
2005
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. , vol. 2. IEEE, 2005, pp. 729–734
2005
Earlier work this paper cites.
H. Zhu, Z. Zhou, R. Yang, and A. Yu, “Discrete particle simulation of particulate systems: theoretical developments,” Chemical Engineering Science , vol. 62, no. 13, pp. 3378–3396, 2007
2007
Earlier work this paper cites.
S. Subramaniam, “Lagrangian–eulerian methods for multiphase flows,” Progress in Energy and Combustion Science , vol. 39, no. 2-3, pp. 215–245, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende et al. , “Interaction networks for learning about objects, relations and physics,” in Advances in neural information processing systems , 2016, pp. 4502–4510
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in neural information processing systems , 2016, pp. 3844–3852
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 2017, pp. 1–14
2017
Earlier work this paper cites.
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , 2017, pp. 1025–1035
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel, “Neural relational inference for interacting systems,” in International Conference on Machine Learning . PMLR, 2018, pp. 2688–2697
2018
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2018, pp. 1–12
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining , 2020, pp. 753–763
2020
Later among the works it cites.
S. Wan, C. Gong, P. Zhong, S. Pan, G. Li, and J. Yang, “Hyperspectral image classification with context-aware dynamic graph convolutional network,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 1, pp. 597–612, 2020
2020
Later among the works it cites.
M. Karamad, R. Magar, Y. Shi, S. Siahrostami, I. D. Gates, and A. B. Farimani, “Orbital graph convolutional neural network for material property prediction,” Physical Review Materials , vol. 4, no. 9, p. 093801, 2020
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Li, J. Wu, R. Tedrake, J. B. Tenenbaum, and A. Torralba, “Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Greydanus, M. Dzamba, and J. Yosinski, “Hamiltonian neural networks,” in Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in International conference on machine learning . PMLR, 2019, pp. 6861–6871
2019
Cited alongside, same era.
J. Klicpera, S. Weißenberger, and S. Günnemann, “Diffusion improves graph learning,” Advances in Neural Information Processing Systems , vol. 32, pp. 13 354–13 366, 2019
2019
Cited alongside, same era.
S. Pan, R. Hu, S.-f. Fung, G. Long, J. Jiang, and C. Zhang, “Learning graph embedding with adversarial training methods,” IEEE transactions on cybernetics , vol. 50, no. 6, pp. 2475–2487, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
C. Herfeld and M. Ivanova, “Introduction: first principles in science—their status and justification,” Synthese , vol. 198, no. 14, pp. 3297–3308, 2021
2021
Later among the works it cites.
J. Shlomi, P. Battaglia, and J.-R. Vlimant, “Graph neural networks in particle physics,” Machine Learning: Science and Technology , vol. 2, no. 2, p. 021001, jan 2021. [Online]. Available: https://doi.org/10.1088/2632-2153/abbf9a
2021
Later among the works it cites.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 1, pp. 4–24, 2021
2021
Later among the works it cites.
K. Martinkus, A. Lucchi, and N. Perraudin, “Scalable graph networks for particle simulations,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 10, 2021, pp. 8912–8920
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Liu, Z. Li, S. Pan, C. Gong, C. Zhou, and G. Karypis, “Anomaly detection on attributed networks via contrastive self-supervised learning,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Later among the works it cites.
S. Ji, S. Pan, E. Cambria, P. Marttinen, and P. S. Yu, “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–21, 2021
2021
Later among the works it cites.
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko et al. , “Highly accurate protein structure prediction with alphafold,” Nature , vol. 596, no. 7873, pp. 583–589, 2021
2021
Later among the works it cites.
Y. Liang, K. Ouyang, H. Yan, Y. Wang, Z. Tong, and R. Zimmermann, “Modeling trajectories with neural ordinary differential equations.” in International Joint Conference on Artificial Intelligence , 2021, pp. 1498–1504
2021
Later among the works it cites.
M. Jin, Y. Zheng, Y.-F. Li, S. Chen, B. Yang, and S. Pan, “Multivariate time series forecasting with dynamic graph neural odes,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
Later among the works it cites.
M. Jin, Y.-F. Li, and S. Pan, “Neural temporal walks: Motif-aware representation learning on continuous-time dynamic graphs,” in Advances in Neural Information Processing Systems , 2022
2022
Later among the works it cites.