Fetching the paper…
Reading the bibliography…
We propose a new family of neural networks to predict the behaviors of physical systems by learning their underpinning constraints.
An introduction to physically based modeling: rigid body simulation ii—nonpenetration constraints
David Baraff · 1997
Earlier work this paper cites.
Large steps in cloth simulation
David Baraff and Andrew Witkin · 1998
Earlier work this paper cites.
A lagrangian network for kinematic control of redundant robot manipulators
Jun Wang, Qingni Hu, and Danchi Jiang · 1999
Earlier work this paper cites.
Robust treatment of collisions, contact and friction for cloth animation
Robert Bridson, Ronald Fedkiw, and John Anderson · 2002
Earlier work this paper cites.
A projection neural network and its application to constrained optimization problems
Youshen Xia, Henry Leung, and Jun Wang · 2002
Earlier work this paper cites.
Nonconvex rigid bodies with stacking
Eran Guendelman, Robert Bridson, and Ronald Fedkiw · 2003
Earlier work this paper cites.
Meshless deformations based on shape matching
Matthias Müller, Bruno Heidelberger, Matthias Teschner, and Markus Gross · 2005
Earlier work this paper cites.
Dynamic simulation of articulated rigid bodies with contact and collision
Rachel Weinstein, Joseph Teran, and Ronald Fedkiw · 2006
Earlier work this paper cites.
Efficient simulation of inextensible cloth
Rony Goldenthal, David Harmon, Raanan Fattal, Michel Bercovier, and Eitan Grinspun · 2007
Earlier work this paper cites.
Position based dynamics
Matthias Müller, Bruno Heidelberger, Marcus Hennix, and John Ratcliff · 2007
Earlier work this paper cites.
Position based fluids
Miles Macklin and Matthias Müller · 2013
Earlier work this paper cites.
A survey on position-based simulation methods in computer graphics
Jan Bender, Matthias Müller, Miguel A Otaduy, Matthias Teschner, and Miles Macklin · 2014
Earlier work this paper cites.
Unified particle physics for real-time applications
Miles Macklin, Matthias Müller, Nuttapong Chentanez, and Tae-Yong Kim · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Position-based simulation methods in computer graphics
Jan Bender, Matthias Müller, and Miles Macklin · 2015
Earlier work this paper cites.
Interaction networks for learning about objects, relations and physics
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Earlier work this paper cites.
Machine learning strategies for systems with invariance properties
Julia Ling, Reese Jones, and Jeremy Templeton · 2016
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum · 2017
Cited alongside, same era.
Visual interaction networks: Learning a physics simulator from video
Nicholas Watters, Daniel Zoran, Theophane Weber, Peter Battaglia, Razvan Pascanu, and Andrea Tacchetti · 2017
Cited alongside, same era.
Neural relational inference for interacting systems
Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel · 2017
Cited alongside, same era.
Vain: Attentional multi-agent predictive modeling
Yedid Hoshen · 2017
Cited alongside, same era.
Discovering objects and their relations from entangled scene representations
Propagation networks for model-based control under partial observation
Yunzhu Li, Jiajun Wu, Jun-Yan Zhu, Joshua B Tenenbaum, Antonio Torralba, and Russ Tedrake · 2019
Later among the works it cites.
Unsupervised discovery of parts, structure, and dynamics
Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T Freeman, Joshua B Tenenbaum, and Jiajun Wu · 2019
Later among the works it cites.
Danilo Jimenez Rezende, Sébastien Racanière, Irina Higgins, and Peter Toth · 2019
Later among the works it cites.
Hamiltonian generative networks
Peter Toth, Danilo Jimenez Rezende, Andrew Jaegle, Sébastien Racanière, Aleksandar Botev, and Irina Higgins · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
David Raposo, Adam Santoro, David Barrett, Razvan Pascanu, Timothy Lillicrap, and Peter Battaglia · 2017
Cited alongside, same era.
Flexible neural representation for physics prediction
Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li F Fei-Fei, Josh Tenenbaum, and Daniel L Yamins · 2018
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Cited alongside, same era.
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia · 2018
Cited alongside, same era.
Augmenting physical simulators with stochastic neural networks: Case study of planar pushing and bouncing
Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie P Kaelbling, Joshua B Tenenbaum, and Alberto Rodriguez · 2018
Cited alongside, same era.
Reasoning about physical interactions with object-oriented prediction and planning
Michael Janner, Sergey Levine, William T Freeman, Joshua B Tenenbaum, Chelsea Finn, and Jiajun Wu · 2018
Cited alongside, same era.
Deep learning of inverse dynamic models
Deep Learning von Inversen Dynamik Modellen · 2018
Cited alongside, same era.
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia · 2019
Later among the works it cites.
Deep lagrangian networks: Using physics as model prior for deep learning
Michael Lutter, Christian Ritter, and Jan Peters · 2019
Later among the works it cites.
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter · 2019
Later among the works it cites.
Chainqueen: A real-time differentiable physical simulator for soft robotics
Yuanming Hu, Jiancheng Liu, Andrew Spielberg, Joshua B Tenenbaum, William T Freeman, Jiajun Wu, Daniela Rus, and Wojciech Matusik · 2019
Later among the works it cites.
Differentiable cloth simulation for inverse problems
Junbang Liang, Ming Lin, and Vladlen Koltun · 2019
Later among the works it cites.
Learning compositional koopman operators for model-based control
Yunzhu Li, Hao He, Jiajun Wu, Dina Katabi, and Antonio Torralba · 2020
Closest in time.
Clevrer: Collision events for video representation and reasoning
Kexin Yi*, Chuang Gan*, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B. Tenenbaum · 2020
Closest in time.
Symplectic recurrent neural networks
Z. Chen, J. Zhang, M. Arjovsky, and L. Bottou · 2020
Closest in time.
Symplectic networks: intrinsic structure-preserving networks for identifying Hamiltonian systems
P. Jin, A. Zhu, G. E. Karniadakis, and Y. Tang · 2020
Closest in time.
Lagrangian neural networks
Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho · 2020
Closest in time.
Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems
Auralee Edelen, Nicole Neveu, Matthias Frey, Yannick Huber, Christopher Mayes, and Andreas Adelmann · 2020
Closest in time.
Learning to control pdes with differentiable physics
Philipp Holl, Vladlen Koltun, and Nils Thuerey · 2020
Closest in time.