Fetching the paper…
Reading the bibliography…
Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to particle mechanics.
Linear representations of finite groups , volume 42
Serre, J.-P. et al · 1977
Earlier work this paper cites.
Development and current status of the charmm force field for nucleic acids
MacKerell Jr, A. D., Banavali, N., and Foloppe, N · 2000
Earlier work this paper cites.
Carnegie-mellon motion capture database
CMU · 2003
Earlier work this paper cites.
How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., and Goodman, N. D · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Battaglia, P. W., Pascanu, R., Lai, M., Rezende, D., and Kavukcuoglu, K · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations
Richard J. Gowers, Max Linke, Jonathan Barnoud, Tyler J. E. Reddy, Manuel N. Melo, Sean L. Seyler, Jan Domański, David L. Dotson, Sébastien Buchoux, Ian M. Kenney, and Oliver Beckstein · 2016
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., and Müller, K.-R · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
Earlier work this paper cites.
Molecular dynamics trajectory for benchmarking mdanalysis
Seyler, S. and Beckstein, O · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Spherical CNNs
Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
Earlier work this paper cites.
Neural relational inference for interacting systems
Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., and Zemel, R · 2018
Cited alongside, same era.
Flexible neural representation for physics prediction
Mrowca, D., Zhuang, C., Wang, E., Haber, N., Fei-Fei, L., Tenenbaum, J. B., and Yamins, D. L · 2018
Cited alongside, same era.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
Cited alongside, same era.
Pointconv: Deep convolutional networks on 3d point clouds, 2018
Wu, W., Qi, Z., and Fuxin, L · 2018
Cited alongside, same era.
Equivariant flows: sampling configurations for multi-body systems with symmetric energies
Learning gradient fields for molecular conformation generation
Shi, C., Luo, S., Xu, M., and Tang, J · 2021
Later among the works it cites.
Seismic wave propagation and inversion with neural operators
Yang, Y., Gao, A. F., Castellanos, J. C., Ross, Z. E., Azizzadenesheli, K., and Clayton, R. W · 2021
Later among the works it cites.
Message passing neural PDE solvers
Brandstetter, J., Worrall, D. E., and Welling, M · 2022
Later among the works it cites.
SE(3) equivariant graph neural networks with complete local frames
Du, W., Zhang, H., Du, Y., Meng, Q., Chen, W., Zheng, N., Shao, B., and Liu, T.-Y · 2022
Later among the works it cites.
Equivariant graph mechanics networks with constraints
Huang, W., Han, J., Rong, Y., Xu, T., Sun, F., and Huang, J · 2022
Later among the works it cites.
Spherical message passing for 3d molecular graphs
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Köhler, J., Klein, L., and Noé, F · 2019
Cited alongside, same era.
Hamiltonian graph networks with ode integrators
Sanchez-Gonzalez, A., Bapst, V., Cranmer, K., and Battaglia, P · 2019
Cited alongside, same era.
Lagrangian fluid simulation with continuous convolutions
Ummenhofer, B., Prantl, L., Thuerey, N., and Koltun, V · 2019
Cited alongside, same era.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F. B., Worrall, D. E., Fischer, V., and Welling, M · 2020
Cited alongside, same era.
Neural operator: Graph kernel network for partial differential equations
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2020
Cited alongside, same era.
Learning mesh-based simulation with graph networks
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W · 2020
Cited alongside, same era.
Learning to simulate complex physics with graph networks
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., and Battaglia, P · 2020
Cited alongside, same era.
Fourier neural operator for parametric partial differential equations
Li, Z., Kovachki, N. B., Azizzadenesheli, K., liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2021
Cited alongside, same era.
Liu, Y., Wang, L., Liu, M., Lin, Y., Zhang, X., Oztekin, B., and Ji, S · 2022
Later among the works it cites.
Accelerating carbon capture and storage modeling using fourier neural operators
Wen, G., Li, Z., Long, Q., Azizzadenesheli, K., Anandkumar, A., and Benson, S. M · 2022
Later among the works it cites.
Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
Later among the works it cites.
Implicit transfer operator learning: Multiple time-resolution surrogates for molecular dynamics
Schreiner, M., Winther, O., and Olsson, S · 2023
Later among the works it cites.
Consistency models
Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
Later among the works it cites.
Spatial attention kinetic networks with e(n)-equivariance
Wang, Y. and Chodera, J · 2023
Later among the works it cites.
Geometric latent diffusion models for 3d molecule generation
Xu, M., Powers, A. S., Dror, R. O., Ermon, S., and Leskovec, J · 2023
Later among the works it cites.
Fast sampling of diffusion models via operator learning
Zheng, H., Nie, W., Vahdat, A., Azizzadenesheli, K., and Anandkumar, A · 2023
Later among the works it cites.
Improved techniques for training consistency models
Song, Y. and Dhariwal, P · 2024
Closest in time.