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G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Rev. Mod. Phys. 91
Original
1903
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H.-Y. Hu, S.-H. Li, L. Wang, and Y.-Z. You, arXiv e-prints , arXiv:1903.00804 (2019), arXiv:1903.00804 [cond-mat.dis-nn]
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1903
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Y.-Z. You, Z. Yang, and X.-L. Qi, Phys. Rev. B 97
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T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, in Advances in neural information processing systems (2018) pp. 6571–6583
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2020

Neural ODE and Holographic QCD

Hashimoto, Koji, Hu, Hong-Ye, You, Yi-Zhuang

Understand

The neural ordinary differential equation (Neural ODE) is a novel machine learning architecture whose weights are smooth functions of the continuous depth.

We apply the Neural ODE to holographic QCD by regarding the weight functions as a bulk metric, and train the machine with lattice QCD data of chiral condensate at finite temperature.
The machine finds consistent bulk geometry at various values of temperature and discovers the emergent black hole horizon in the holographic bulk automatically.
The holographic Wilson loops calculated with the emergent machine-learned bulk spacetime have consistent temperature dependence of confinement and Debye-screening behavior.

Built on

G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborová, Rev. Mod. Phys. 91
Original
1903
Earlier work this paper cites.
H.-Y. Hu, S.-H. Li, L. Wang, and Y.-Z. You, arXiv e-prints , arXiv:1903.00804 (2019), arXiv:1903.00804 [cond-mat.dis-nn]
Original
1903
Earlier work this paper cites.
K. Hashimoto, Phys. Rev. D 99
Original
1903
Earlier work this paper cites.
S. Gubser, I. R. Klebanov, and A. M. Polyakov, Phys. Lett. B 428
1998
Earlier work this paper cites.
E. Witten, Adv. Theor. Math. Phys. 2
1998
Earlier work this paper cites.
J. M. Maldacena, Phys. Rev. Lett. 80
1998
Earlier work this paper cites.
S.-J. Rey, S. Theisen, and J.-T. Yee, Nucl. Phys. B 527
1998
Earlier work this paper cites.
J. M. Maldacena, Int. J. Theor. Phys. 38
1999
Earlier work this paper cites.
S.-J. Rey and J.-T. Yee, Eur. Phys. J. C 22
2001
Earlier work this paper cites.

Similar

Y.-K. Yan, S.-F. Wu, X.-H. Ge, and Y. Tian, (2020), arXiv:2004.12112 [hep-th]
Original
2004
Cited alongside, same era.
T. Akutagawa, K. Hashimoto, and T. Sumimoto, (2020), arXiv:2005.02636 [hep-th]
Original
2005
Cited alongside, same era.
O. Andreev and V. I. Zakharov, JHEP 04
2007
Cited alongside, same era.
U. Gursoy and E. Kiritsis, JHEP 02
Original
2008
Cited alongside, same era.
U. Gursoy, E. Kiritsis, and F. Nitti, JHEP 02
Original
2008
Cited alongside, same era.

Then

W.-C. Gan and F.-W. Shu, ArXiv e-prints (2017), arXiv:1705.05750 [gr-qc]
Original
2017
Later among the works it cites.
J. Liu, Y. Qi, Z. Y. Meng, and L. Fu, Physical Review B 95
2017
Later among the works it cites.
Y.-Z. You, Z. Yang, and X.-L. Qi, Phys. Rev. B 97
Original
2018
Later among the works it cites.
T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, in Advances in neural information processing systems (2018) pp. 6571–6583
2018
Later among the works it cites.
R. Vasseur, A. C. Potter, Y.-Z. You, and A. W. Ludwig, Phys. Rev. B 100
Original
2019
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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F. Karsch (RBC-Bielefeld), Nucl. Phys. A 820

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P. Petreczky, Mod. Phys. Lett. A 25

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K. He, X. Zhang, S. Ren, and J. Sun, arXiv e-prints , arXiv:1512.03385 (2015), arXiv:1512.03385 [cs.CV]

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K. Hashimoto, S. Sugishita, A. Tanaka, and A. Tomiya, Phys. Rev. D 98

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