Built on

K. Hashimoto, “AdS/CFT correspondence as a deep Boltzmann machine,” Phys. Rev. D 99
Original
1903
Earlier work this paper cites.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto and L. Zdeborová, “Machine learning and the physical sciences,” Rev. Mod. Phys. 91
Original
1903
Earlier work this paper cites.
H. Hu, S. Li, L. Wang and Y. You, “Machine Learning Holographic Mapping by Neural Network Renormalization Group,” [arXiv:1903.00804 [cond-mat.dis-nn]]
Original
1903
Earlier work this paper cites.
J. Tan and C. Chen, “Deep learning the holographic black hole with charge,” Int. J. Mod. Phys. D 28
Original
1908
Earlier work this paper cites.
J. M. Maldacena, “The Large N limit of superconformal field theories and supergravity,” Int. J. Theor. Phys. 38
1998
Earlier work this paper cites.
S. S. Gubser, I. R. Klebanov and A. M. Polyakov, “Gauge theory correlators from noncritical string theory,” Phys. Lett. B 428
1998
Earlier work this paper cites.
E. Witten, “Anti-de Sitter space and holography,” Adv. Theor. Math. Phys. 2
1998
Earlier work this paper cites.
Y. Yan, S. Wu, X. Ge and Y. Tian, “Deep Learning black hole metrics from shear viscosity,” [arXiv:2004.12112 [hep-th]]
Original
2004
Earlier work this paper cites.
J. Erlich, E. Katz, D. T. Son and M. A. Stephanov, “QCD and a holographic model of hadrons,” Phys. Rev. Lett. 95
2005
Earlier work this paper cites.
L. Da Rold and A. Pomarol, “Chiral symmetry breaking from five dimensional spaces,” Nucl. Phys. B 721
2005
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks.,” Science 313, 504 (2006)
2006
Earlier work this paper cites.

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2008
Cited alongside, same era.

Then

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Later among the works it cites.
Y. H. He, “Machine-learning the string landscape,” Phys. Lett. B 774
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2017
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F. Ruehle, “Evolving neural networks with genetic algorithms to study the String Landscape,” JHEP 1708
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2020

Deep Learning and AdS/QCD

Akutagawa, Tetsuya, Hashimoto, Koji, Sumimoto, Takayuki

Understand

We propose a deep learning method to build an AdS/QCD model from the data of hadron spectra.

A major problem of generic AdS/QCD models is that a large ambiguity is allowed for the bulk gravity metric with which QCD observables are holographically calculated.
We adopt the experimentally measured spectra of $\rho$ and $a_2$ mesons as training data, and perform a supervised machine learning which determines concretely a bulk metric and a dilaton profile of an AdS/QCD model.
Our deep learning (DL) architecture is based on the AdS/DL correspondence (arXiv:1802.08313) where the deep neural network is identified with the emergent bulk spacetime.

Built on

K. Hashimoto, “AdS/CFT correspondence as a deep Boltzmann machine,” Phys. Rev. D 99
Original
1903
Earlier work this paper cites.
G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto and L. Zdeborová, “Machine learning and the physical sciences,” Rev. Mod. Phys. 91
Original
1903
Earlier work this paper cites.
H. Hu, S. Li, L. Wang and Y. You, “Machine Learning Holographic Mapping by Neural Network Renormalization Group,” [arXiv:1903.00804 [cond-mat.dis-nn]]
Original
1903
Earlier work this paper cites.
J. Tan and C. Chen, “Deep learning the holographic black hole with charge,” Int. J. Mod. Phys. D 28
Original
1908
Earlier work this paper cites.
J. M. Maldacena, “The Large N limit of superconformal field theories and supergravity,” Int. J. Theor. Phys. 38
1998
Earlier work this paper cites.
S. S. Gubser, I. R. Klebanov and A. M. Polyakov, “Gauge theory correlators from noncritical string theory,” Phys. Lett. B 428
1998
Earlier work this paper cites.
E. Witten, “Anti-de Sitter space and holography,” Adv. Theor. Math. Phys. 2
1998
Earlier work this paper cites.
Y. Yan, S. Wu, X. Ge and Y. Tian, “Deep Learning black hole metrics from shear viscosity,” [arXiv:2004.12112 [hep-th]]
Original
2004
Earlier work this paper cites.
J. Erlich, E. Katz, D. T. Son and M. A. Stephanov, “QCD and a holographic model of hadrons,” Phys. Rev. Lett. 95
2005
Earlier work this paper cites.
L. Da Rold and A. Pomarol, “Chiral symmetry breaking from five dimensional spaces,” Nucl. Phys. B 721
2005
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks.,” Science 313, 504 (2006)
2006
Earlier work this paper cites.

Similar

A. Karch, E. Katz, D. T. Son and M. A. Stephanov, “Linear confinement and AdS/QCD,” Phys. Rev. D 74
2006
Cited alongside, same era.
E. Katz, A. Lewandowski and M. D. Schwartz, “Tensor mesons in AdS/QCD,” Phys. Rev. D 74
2006
Cited alongside, same era.
Y. Bengio, Y. LeCun, “Scaling learning algorithms towards AI,” Large-scale kernel machines 34 (2007)
2007
Cited alongside, same era.
U. Gursoy and E. Kiritsis, “Exploring improved holographic theories for QCD: Part I,” JHEP 02
Original
2008
Cited alongside, same era.
U. Gursoy, E. Kiritsis and F. Nitti, “Exploring improved holographic theories for QCD: Part II,” JHEP 02
Original
2008
Cited alongside, same era.