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Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics.
Invariant variation problems
Noether, E · 1971
Earlier work this paper cites.
Hamiltonian fluid mechanics
Salmon, R · 1988
Earlier work this paper cites.
Modern quantum mechanics, revised edition
Sakurai, J. J. and Commins, E. D · 1995
Earlier work this paper cites.
Photons and atoms-introduction to quantum electrodynamics
Cohen-Tannoudji, C., Dupont-Roc, J., and Grynberg, G · 1997
Earlier work this paper cites.
A modern course in statistical physics
Reichl, L. E · 1999
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
Tenenbaum, J. B., De Silva, V., and Langford, J. C · 2000
Earlier work this paper cites.
Classical mechanics
Taylor, J. R · 2005
Earlier work this paper cites.
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Pukrittayakamee, A., Malshe, M., Hagan, M., Raff, L., Narulkar, R., Bukkapatnum, S., and Komanduri, R · 2009
Earlier work this paper cites.
Distilling free-form natural laws from experimental data
Schmidt, M. and Lipson, H · 2009
Earlier work this paper cites.
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J · 2011
Earlier work this paper cites.
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Yosinski, J., Clune, J., Hidalgo, D., Nguyen, S., Zagal, J. C., and Lipson, H · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G · 2012
Earlier work this paper cites.
Fast and accurate modeling of molecular atomization energies with machine learning
Rupp, M., Tkatchenko, A., Müller, K.-R., and Von Lilienfeld, O. A · 2012
Earlier work this paper cites.
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
High-dimensional neural network potentials for organic reactions and an improved training algorithm
Gastegger, M. and Marquetand, P · 2015
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Interaction networks for learning about objects, relations and physics
Battaglia, P., Pascanu, R., Lai, M., Rezende, D. J., et al · 2016
Cited alongside, same era.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
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Cited alongside, same era.
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
Cited alongside, same era.
End-to-end differentiable physics for learning and control
de Avila Belbute-Peres, F., Smith, K., Allen, K., Tenenbaum, J., and Kolter, J. Z · 2018
Later among the works it cites.
Recurrent world models facilitate policy evolution
Ha, D. and Schmidhuber, J · 2018
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Learning latent dynamics for planning from pixels
Hafner, D., Lillicrap, T., Fischer, I., Villegas, R., Ha, D., Lee, H., and Davidson, J · 2018
Later among the works it cites.
Relational inductive bias for physical construction in humans and machines
Hamrick, J. B., Allen, K. R., Bapst, V., Zhu, T., McKee, K. R., Tenenbaum, J. B., and Battaglia, P. W · 2018
Later among the works it cites.
Discovering physical concepts with neural networks
Iten, R., Metger, T., Wilming, H., Del Rio, L., and Renner, R · 2018
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Santoro, A., Raposo, D., Barrett, D. G., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017
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Cited alongside, same era.
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