Citing Articles For: Machine learning holographic black hole from lattice QCD equation of state

Bayesian inference of the magnetic field and chemical potential on holographic jet quenching in heavy-ion collisions*

Liqiang 力强 Zhu 朱, Zhan 湛 Gao 高, Weiyao 伟尧 Ke 柯, and Hanzhong 汉中 Zhang 张

Chinese Phys. C 50 044108 (2026)

Schwinger effect in a twice anisotropic holographic model*

Wen-Bin 文彬 Chang 常 and Defu 德富 Hou 侯

Chinese Phys. C 50 053103 (2026)

Discovering the Gell-Mann–Okubo formula with Kolmogorov-Arnold networks*

Jian-Yao 渐尧 He 贺, Xun 勋 Chen 陈, Xiao-Yan 小彦 Zhu 朱, and Wen 文 Luo 罗

Chinese Phys. C 50 103107 (2026)

Bayesian inference of the critical end point in a ( 2+1 )-flavor system from holographic QCD

Liqiang Zhu, Xun Chen, Kai Zhou, Hanzhong Zhang, and Mei Huang

Phys. Rev. D 112 026019 (2025)

Physics-informed neural network approach to quark-antiquark color flux tube

Wei Kou, Xiaoxuan Lin, Bing’ang Guo, and Xurong Chen

Phys. Rev. D 112 056027 (2025)

Thermal properties of the scalar glueballs from holography

Ruixiang Chen, Danning Li, and Mei Huang

Phys. Rev. D 112 094021 (2025)

Deep learning-based holography for T -linear resistivity

Byoungjoon Ahn, Hyun-Sik Jeong, Chang-Woo Ji, Keun-Young Kim, and Kwan Yun

Phys. Rev. D 112 126008 (2025)

Data-driven Einstein-dilaton model for pure Yang-Mills thermodynamics and glueball spectrum

Xun Chen, Yidian Chen, and Kai Zhou

Phys. Rev. D 112 126025 (2025)

Impact of the phase transition on quark-gluon plasma with an extremely strong magnetic field in holographic QCD

Xuanmin Cao and Hui Liu

Phys. Rev. D 111 026008 (2025)

Holographic Schwinger effect in flavor-dependent systems

Sheng Lin, Xuan Liu, Xun Chen, Gen-Fa Zhang, and Jing Zhou

Phys. Rev. D 111 046005 (2025)

Gluon polarization contribution to the spin alignment of vector mesons from holography

Hiwa A. Ahmed, Yidian Chen, and Mei Huang

Phys. Rev. D 111 086006 (2025)

Exploring transport properties of quark-gluon plasma in flavor-dependent systems with a holographic model

Bing Chen, Xun Chen, Xiaohua Li, Zhou-Run Zhu, and Kai Zhou

Phys. Rev. D 111 086033 (2025)

Light quark energy loss in the flavor-dependent systems from holography

Le Zhang, Lu Yin, Guo-Dong Zhou, Chao-Jie Fan, and Xun Chen

Phys. Rev. D 111 126001 (2025)

Jet quenching and quark energy loss from holographic QCD

Zhibin Li, Danning Li, and Mei Huang

Phys. Rev. D 111 126019 (2025)

Net proton number cumulants from viscous hydrodynamics with an equation of state including a critical end point

Yifan Shen, Wei Chen, Xiang-Yu Wu, Kun Xu, and Mei Huang

Phys. Rev. C 111 014916 (2025)

Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments

Koji Hashimoto, Koshiro Matsuo, Masaki Murata, Gakuto Ogiwara, and Daichi Takeda

Mach. Learn.: Sci. Technol. 6 015030 (2025)

Flavor dependent critical endpoint from holographic QCD through machine learning

Xun Chen and Mei Huang

J. High Energ. Phys. 2025 123 (2025)

The underlying black hole phase transitions in an Einstein-Maxwell-dilaton model with a holographic critical point

Hong Guo, Xiao-Mei Kuang, and Wei-Liang Qian

J. High Energ. Phys. 2025 142 (2025)

Revisiting holographic model for thermal and dense QCD with a critical point

Qingxuan Fu, Song He, Li Li, and Zhibin Li

J. High Energ. Phys. 2025 221 (2025)

Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy

Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim, and Kwan Yun

J. High Energ. Phys. 2025 25 (2025)

Holographic flavour and neural networks

Veselin G. Filev

J. High Energ. Phys. 2025 31 (2025)

Machine learning insights into quark–antiquark interactions: probing field distributions and string tension in QCD

Wei Kou and Xurong Chen

Eur. Phys. J. C 85 261 (2025)

AInstein: numerical Einstein metrics via machine learning

Edward Hirst, Tancredi Schettini Gherardini, and Alexander G Stapleton

AI Sci. 1 025001 (2025)

Holographic entanglement properties in the QCD phase diagram from Einstein-Maxwell-dilaton models

Zhibin Li

Phys. Rev. D 110 046012 (2024)

Potential energy of heavy quarkonium in flavor-dependent systems from a holographic model

Xi Guo, Xun Chen, Dong Xiang, Miguel Angel Martin Contreras, and Xiao-Hua Li

Phys. Rev. D 110 046014 (2024)

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Articles Citing

Machine learning holographic black hole from lattice QCD equation of state

Bayesian inference of the magnetic field and chemical potential on holographic jet quenching in heavy-ion collisions*

Schwinger effect in a twice anisotropic holographic model*

Discovering the Gell-Mann–Okubo formula with Kolmogorov-Arnold networks*

Bayesian inference of the critical end point in a ( 2+1 )-flavor system from holographic QCD

Physics-informed neural network approach to quark-antiquark color flux tube

Thermal properties of the scalar glueballs from holography

Deep learning-based holography for T -linear resistivity

Data-driven Einstein-dilaton model for pure Yang-Mills thermodynamics and glueball spectrum

Impact of the phase transition on quark-gluon plasma with an extremely strong magnetic field in holographic QCD

Holographic Schwinger effect in flavor-dependent systems

Gluon polarization contribution to the spin alignment of vector mesons from holography

Exploring transport properties of quark-gluon plasma in flavor-dependent systems with a holographic model

Light quark energy loss in the flavor-dependent systems from holography

Jet quenching and quark energy loss from holographic QCD

Net proton number cumulants from viscous hydrodynamics with an equation of state including a critical end point

Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments

Flavor dependent critical endpoint from holographic QCD through machine learning

The underlying black hole phase transitions in an Einstein-Maxwell-dilaton model with a holographic critical point

Revisiting holographic model for thermal and dense QCD with a critical point

Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy

Holographic flavour and neural networks

Machine learning insights into quark–antiquark interactions: probing field distributions and string tension in QCD

AInstein: numerical Einstein metrics via machine learning

Holographic entanglement properties in the QCD phase diagram from Einstein-Maxwell-dilaton models

Potential energy of heavy quarkonium in flavor-dependent systems from a holographic model

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