Pole-skipping data encodes enough information to reconstruct the full metric of 3D rotating black holes and the radial functions of 4D separable rotating black holes, with Einstein equations becoming algebraic constraints on that data.
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Neural networks reconstruct both spatial and timelike bulk metric components from strip entanglement entropy and Wilson loops with sub-0.2% accuracy in holographic models such as AdS-Schwarzschild and Gubser-Rocha.
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Probing bulk geometry via pole skipping: from static to rotating spacetimes
Pole-skipping data encodes enough information to reconstruct the full metric of 3D rotating black holes and the radial functions of 4D separable rotating black holes, with Einstein equations becoming algebraic constraints on that data.
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Holographic entanglement entropy, Wilson loops, and neural networks
Neural networks reconstruct both spatial and timelike bulk metric components from strip entanglement entropy and Wilson loops with sub-0.2% accuracy in holographic models such as AdS-Schwarzschild and Gubser-Rocha.
- Learning holographic QCD with unflavored meson spectra