A recent burst of activity in applying machine learning to tackle fundamental questions in physics suggests that associated techniques may soon become as common in physics as numerical simulations or calculus.
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References
LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).
Silver, D. et al. Nature 529, 484–489 (2016).
Carrasquilla, J. & Melko, R. G. Nat. Phys. 16, 431–434 (2017).
van Nieuwenburg, E. P. L., Liu, Y.-H. & Huber, S. D . Nat. Phys. 16, 435–439 (2017).
Carleo, G. & Troyer, M. Preprint at http://arxiv.org/pdf/1606.02318.pdf (2016).
Schoenholz, S. S., Cubuk, E. D., Sussman, D. M., Kaxiras, E. & Liu, A. J. Nat. Phys. 12, 469–471 (2016).
Hirn, M., Poilvert, N. & Mallat, S. Preprint at http://arxiv.org/pdf/1502.02077.pdf (2015).
Arsenault, L.-F., von Lilienfeld, O. A. & Millis, A. J. Preprint at http://arxiv.org/pdf/1506.08858.pdf (2015).
Hansen, K. et al. J. Chem. Theor. Comp. 9, 3404–3419 (2013).
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Zdeborová, L. New tool in the box. Nature Phys 13, 420–421 (2017). https://doi.org/10.1038/nphys4053
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DOI: https://doi.org/10.1038/nphys4053
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