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Deep learning black hole metrics from shear viscosity

机译：深度学习剪切粘度的黑洞指标

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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?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.
机译：基于广告/ CFT对应，我们建立一个深度神经网络，从复杂的频率依赖性剪切粘度学习黑洞指标。网络架构提供了剪切粘度的全息重整化组流的离散化表示，并且可以应用于大类强耦合的域理论。鉴于地平线的存在并引导时空的光滑度，我们表明Schwarzschild和Reissner-Nordstr？M个指标可以准确学习。此外，我们说明了深神经网络的泛化能力可以是优异的，这表明通过使用黑洞时空作为隐藏数据结构，可以从窄频率范围产生宽频谱的剪切粘度。这些结果进一步推广到爱因斯坦-Maxwell-Dilaton黑洞。我们的工作可能不仅可以建议学习全息传输的数据驱动方式，而且还阐明了全息二元性和深度学习。

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