Deep learning bulk spacetime from boundary optical conductivity

Abstract

We employ a deep learning method to deduce the \textit{bulk} spacetime from \textit{boundary} optical conductivity. We apply the neural ordinary differential equation technique, tailored for continuous functions such as the metric, to the typical class of holographic condensed matter models featuring broken translations: linear-axion models. We successfully extract the bulk metric from the boundary holographic optical conductivity. Furthermore, as an example for real material, we use experimental optical conductivity of ${\text{UPd}}_2{\text{Al}}_3$, a representative of heavy fermion metals in strongly correlated electron systems, and construct the corresponding bulk metric. To our knowledge, our work is the first illustration of deep learning bulk spacetime from \textit{boundary} holographic or experimental conductivity data.

30 pages, 8 figures

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• Deep learning-based holography for T-linear resistivity
• The Algebraic Structure Underlying Pole-Skipping Points
• Holographic geometry/real-space entanglement correspondence and metric reconstruction
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