High Energy / Nuclear Theory / RIKEN seminars
# [Riken seminar] Deep learning black hole metrics from shear viscosity

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by

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US/Eastern

Description

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.

BJ link: https://bluejeans.com/871723105