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Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors.
MONet: Unsupervised scene decomposition and representation (2019)
Burgess, C. P. et al · 1901
Earlier work this paper cites.
Surface simplification using quadric error metrics
Garland, M. & Heckbert, P. S · 1997
Earlier work this paper cites.
Open Dynamics Engine (2005)
Smith, R · 2005
Earlier work this paper cites.
Meshless deformations based on shape matching
Müller, M., Heidelberger, B., Teschner, M. & Gross, M · 2005
Earlier work this paper cites.
Nvidia PhysX system software (2008)
Corporation, N · 2008
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M. & Monfardini, G · 2008
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T. & Tassa, Y · 2012
Earlier work this paper cites.
Adaptive anisotropic remeshing for cloth simulation
Narain, R., Samii, A. & O’brien, J. F · 2012
Earlier work this paper cites.
Bullet physics simulation
Coumans, E · 2015
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Battaglia, P., Pascanu, R., Lai, M., Jimenez Rezende, D. et al · 2016
Earlier work this paper cites.
Attention is all you need
Vaswani, A. et al · 2017
Earlier work this paper cites.
Topologynet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions
Cang, Z. & Wei, G.-W · 2017
Earlier work this paper cites.
Flexible neural representation for physics prediction
Mrowca, D. et al · 2018
Earlier work this paper cites.
Relational inductive biases, deep learning, and graph networks (2018)
Battaglia, P. W. et al · 2018
Earlier work this paper cites.
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R · 2018
Earlier work this paper cites.
Propagation networks for model-based control under partial observation
Li, Y. et al · 2019
Earlier work this paper cites.
Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids
Li, Y., Wu, J., Tedrake, R., Tenenbaum, J. B. & Torralba, A · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A. et al · 2019
Cited alongside, same era.
Object-centric learning with slot attention
Locatello, F. et al · 2020
Cited alongside, same era.
Learning to simulate complex physics with graph networks
Sanchez-Gonzalez, A. et al · 2020
Cited alongside, same era.
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Oono, K. & Suzuki, T · 2020
Cited alongside, same era.
Learning mesh-based simulation with graph networks
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A. & Battaglia, P. W · 2021
Cited alongside, same era.
Fundamental challenges in deep learning for stiff contact dynamics
Parmar, M., Halm, M. & Posa, M · 2021
Cited alongside, same era.
Comphy: Compositional physical reasoning of objects and events from videos
Chen, Z. et al · 2022
Later among the works it cites.
Learning physics-consistent particle interactions
Han, Z., Kammer, D. S. & Fink, O · 2022
Later among the works it cites.
Learning physical dynamics with subequivariant graph neural networks
Han, J. et al · 2022
Later among the works it cites.
Geometry-enhanced molecular representation learning for property prediction
Fang, X. et al · 2022
Later among the works it cites.
Topological Deep Learning: Graphs, Complexes, Sheaves
Bodnar, C · 2022
Later among the works it cites.
Kubric: A scalable dataset generator
Greff, K. et al · 2022
Later among the works it cites.
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Bear, D. et al · 2021
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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