Deep learning black hole metrics from shear viscosity
| Autor: | Yan, Yu-Kun, Wu, Shao-Feng, Ge, Xian-Hui, Tian, Yu |
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| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Physical Review D 102, 101902(R) (2020) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1103/PhysRevD.102.101902 |
| Popis: | Based on AdS/CFT correspondence, we build a deep neural network to learn black hole metrics from the complex frequency-dependent shear viscosity. The network architecture provides a discretized representation of the holographic renormalization group flow of the shear viscosity and can be applied to a large class of strongly coupled field theories. Given the existence of the horizon and guided by the smoothness of spacetime, we show that Schwarzschild and Reissner-Nordstr\"{o}m metrics can be learned accurately. Moreover, we illustrate that the generalization ability of the deep neural network can be excellent, which indicates that by using the black hole spacetime as a hidden data structure, a wide spectrum of the shear viscosity can be generated from a narrow frequency range. These results are further generalized to an Einstein-Maxwell-dilaton black hole. Our work might not only suggest a data-driven way to study holographic transports but also shed some light on holographic duality and deep learning. Comment: 6+6 pages, 4 figures, 1 table |
| Databáze: | arXiv |
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