Knowledge Management System of Institute of Theoretical Physics, CAS
| Yan, Yu-Kun; Wu, Shao-Feng; Ge, Xian-Hui; Tian, Yu1,2,3,4 | |
| Deep learning black hole metrics from shear viscosity | |
| Source Publication | PHYSICAL REVIEW D
 |
| Language | 英语 |
| Keyword | RENORMALIZATION-GROUP SPACETIME |
| Abstract | 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. |
| 2020 | |
| ISSN | 2470-0010 |
| Volume | 102Issue:10Pages:101902 |
| Cooperation Status | 国际 |
| Subject Area | Astronomy & Astrophysics ; Physics |
| MOST Discipline Catalogue | Astronomy & Astrophysics ; Physics, Particles & Fields |
| DOI | 10.1103/PhysRevD.102.101902 |
| Indexed By | SCIE |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.itp.ac.cn/handle/311006/27204 |
| Collection | SCI期刊论文 |
| Affiliation | 1.Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China 2.Univ Chinese Acad Sci, Sch Phys, Beijing 100049, Peoples R China 3.Yangzhou Univ, Ctr Gravitat & Cosmol, Yangzhou 225009, Peoples R China 4.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China 5.MIT, Ctr Theoret Phys, Cambridge, MA 02139 USA |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| Deep learning black （448KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | Application Full Text | |
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