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.
Deep learning- based holography for t-linear resistivity
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A neural network learns holographic bulk functions from lattice QCD data at zero chemical potential and embeds them into an EMD model to describe finite-density QCD and locate the critical end point.
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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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HoloNet: Toward a Unified Einstein-Maxwell-Dilaton Framework of QCD
A neural network learns holographic bulk functions from lattice QCD data at zero chemical potential and embeds them into an EMD model to describe finite-density QCD and locate the critical end point.