Deep learning black hole metrics from shear viscosity
文献类型：期刊论文
| 作者 | Yan, Yu-Kun; Wu, Shao-Feng; Ge, Xian-Hui; Tian, Yu2,3,4,5 |
| 刊名 | PHYSICAL REVIEW D
 |
| 出版日期 | 2020 |
| 卷号 | 102期号:10页码:101902 |
| 关键词 | RENORMALIZATION-GROUP SPACETIME |
| ISSN号 | 2470-0010 |
| DOI | 10.1103/PhysRevD.102.101902 |
| 英文摘要 | 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-Nordstrom 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. |
| 学科主题 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| 源URL | [http://ir.itp.ac.cn/handle/311006/27204]  |
| 专题 | 理论物理研究所_理论物理所1978-2010年知识产出 |
| 作者单位 | 1.MIT, Ctr Theoret Phys, Cambridge, MA 02139 USA 2.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China 3.Yangzhou Univ, Ctr Gravitat & Cosmol, Yangzhou 225009, Peoples R China 4.Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China 5.Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Yu-Kun,Wu, Shao-Feng,Ge, Xian-Hui,et al. Deep learning black hole metrics from shear viscosity[J]. PHYSICAL REVIEW D,2020,102(10):101902. |
| APA | Yan, Yu-Kun,Wu, Shao-Feng,Ge, Xian-Hui,&Tian, Yu.(2020).Deep learning black hole metrics from shear viscosity.PHYSICAL REVIEW D,102(10),101902. |
| MLA | Yan, Yu-Kun,et al."Deep learning black hole metrics from shear viscosity".PHYSICAL REVIEW D 102.10(2020):101902. |
入库方式： OAI收割
来源：理论物理研究所
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