论文标题
深度学习的黑洞指标来自剪切粘度
Deep learning black hole metrics from shear viscosity
论文作者
论文摘要
基于ADS/CFT对应关系，我们建立了一个深神网络，以从复杂的频率依赖性剪切粘度中学习黑洞指标。网络架构提供了剪切粘度的全息重新归一化组流的离散表示，并可以应用于大型强耦合场理论。鉴于地平线的存在并在时空的平稳性的指导下，我们表明Schwarzschild和Reissner-Nordström指标可以准确地学习。此外，我们说明，深神经网络的概括能力可以很好，这表明通过将黑洞时空用作隐藏的数据结构，可以从狭窄的频率范围内生成较大的剪切粘度。这些结果进一步概括为爱因斯坦 - 马克斯韦尔 - 迪拉塔顿黑洞。我们的工作不仅可能提出了一种研究全息运输的数据驱动方式，而且还阐明了全息二元性和深度学习。
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-Nordström 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.
