Thursday, June 18, 2020, 9:00 am — https://bluejeans.com/871723105
Based on the AdS/CFT correspondence, we build up a simple deep neural network to learn the 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 is applicable for a large class of strongly coupled field theories. Given the existence of the horizon and guided by the smoothness of spacetimes, we show that the 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 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. Our work might not only suggest a data-driven way to study holographic transports, but also shed new light on the emergence mechanism of black hole spacetimes from field theories.
Hosted by: Nikhil Karthik
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