Neural ODEs for holographic transport models without translation symmetry
Abstract We investigate the data-driven holographic transport models without translation symmetry, focusing on the real part of frequency-dependent shear viscosity, $$\eta _{\textrm{re}}(\omega )$$ η re ( ω ) . We develop a radial flow equation of the shear response and establish its relation to $$\...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-01-01
|
| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-025-13759-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832585455359492096 |
|---|---|
| author | Zhuo-Fan Gu Yu-Kun Yan Shao-Feng Wu |
| author_facet | Zhuo-Fan Gu Yu-Kun Yan Shao-Feng Wu |
| author_sort | Zhuo-Fan Gu |
| collection | DOAJ |
| description | Abstract We investigate the data-driven holographic transport models without translation symmetry, focusing on the real part of frequency-dependent shear viscosity, $$\eta _{\textrm{re}}(\omega )$$ η re ( ω ) . We develop a radial flow equation of the shear response and establish its relation to $$\eta _{\textrm{re} }(\omega )$$ η re ( ω ) for a wide class of holographic models. This allows us to determine $$\eta _{\textrm{re}}(\omega )$$ η re ( ω ) of a strongly coupled field theory by the black hole metric and the graviton mass. The latter serves as the bulk dual to the translation symmetry breaking on the boundary. We convert the flow equation to a Neural Ordinary Differential Equation (Neural ODE), which is a neural network with continuous depth and produces output through a black-box ODE solver. Testing the Neural ODE on three well-known holographic models without translation symmetry, we demonstrate its ability to accurately learn either the metric or mass when given the other. By specifying the metric to be asymptotically AdS, we also train the Neural ODE to learn the metric and mass simultaneously. Furthermore, we apply the Neural ODE to the experimental data from a two-dimensional liquid strongly coupled dusty plasma. Interestingly, the emergent metric exhibits non-monotonic behavior, and the mass squared is negative. |
| format | Article |
| id | doaj-art-cfb9c3ecd69c4c0cb0bf9894636e0a88 |
| institution | Kabale University |
| issn | 1434-6052 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj-art-cfb9c3ecd69c4c0cb0bf9894636e0a882025-01-26T12:49:23ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522025-01-0185111910.1140/epjc/s10052-025-13759-0Neural ODEs for holographic transport models without translation symmetryZhuo-Fan Gu0Yu-Kun Yan1Shao-Feng Wu2Department of Physics, Shanghai UniversitySchool of Physics, University of Chinese Academy of SciencesDepartment of Physics, Shanghai UniversityAbstract We investigate the data-driven holographic transport models without translation symmetry, focusing on the real part of frequency-dependent shear viscosity, $$\eta _{\textrm{re}}(\omega )$$ η re ( ω ) . We develop a radial flow equation of the shear response and establish its relation to $$\eta _{\textrm{re} }(\omega )$$ η re ( ω ) for a wide class of holographic models. This allows us to determine $$\eta _{\textrm{re}}(\omega )$$ η re ( ω ) of a strongly coupled field theory by the black hole metric and the graviton mass. The latter serves as the bulk dual to the translation symmetry breaking on the boundary. We convert the flow equation to a Neural Ordinary Differential Equation (Neural ODE), which is a neural network with continuous depth and produces output through a black-box ODE solver. Testing the Neural ODE on three well-known holographic models without translation symmetry, we demonstrate its ability to accurately learn either the metric or mass when given the other. By specifying the metric to be asymptotically AdS, we also train the Neural ODE to learn the metric and mass simultaneously. Furthermore, we apply the Neural ODE to the experimental data from a two-dimensional liquid strongly coupled dusty plasma. Interestingly, the emergent metric exhibits non-monotonic behavior, and the mass squared is negative.https://doi.org/10.1140/epjc/s10052-025-13759-0 |
| spellingShingle | Zhuo-Fan Gu Yu-Kun Yan Shao-Feng Wu Neural ODEs for holographic transport models without translation symmetry European Physical Journal C: Particles and Fields |
| title | Neural ODEs for holographic transport models without translation symmetry |
| title_full | Neural ODEs for holographic transport models without translation symmetry |
| title_fullStr | Neural ODEs for holographic transport models without translation symmetry |
| title_full_unstemmed | Neural ODEs for holographic transport models without translation symmetry |
| title_short | Neural ODEs for holographic transport models without translation symmetry |
| title_sort | neural odes for holographic transport models without translation symmetry |
| url | https://doi.org/10.1140/epjc/s10052-025-13759-0 |
| work_keys_str_mv | AT zhuofangu neuralodesforholographictransportmodelswithouttranslationsymmetry AT yukunyan neuralodesforholographictransportmodelswithouttranslationsymmetry AT shaofengwu neuralodesforholographictransportmodelswithouttranslationsymmetry |