Learning geometries beyond asymptotic AdS
Cheng Ran, Shao-Feng Wu, and Zhuo-Yu Xian

TL;DR
This paper introduces a machine learning approach for reconstructing bulk geometries in holography beyond asymptotic AdS, successfully applying it to models like SYK and identifying critical geometries.
Contribution
The authors develop a neural ODE-based method for bulk reconstruction from boundary data, extending holographic techniques to non-AdS spacetimes and applying it to the SYK model.
Findings
Successfully reconstructs AdS, Lifshitz, and hyperscaling geometries.
Identifies a critical curve for the SYK model where the geometry resembles an AdS$_2$ black hole.
Establishes an effective bulk dual of the SYK coupling.
Abstract
We present a data-driven method for holographic bulk reconstruction that works even when the spacetime is not asymptotically AdS. Given the data of boundary Green functions within a finite frequency window, we iteratively adjust a bulk metric with a finite radial cutoff until its holographic Green functions reproduce the boundary data. Based on the holographic Wilsonian renormalization group for the Klein-Gordon equation in an undetermined curve space, we construct a radial flow equation and transform it into a Neural ODE, which is an infinite-depth neural network for modeling continuous dynamics. Assuming the double-trace coupling in the Wilsonian action is real, we demonstrate that the Neural ODE can effectively learn the metrics with AdS, Lifshitz, and hyperscaling violated asymptotics. In particular, we apply the algorithm to the Sachdev-Ye-Kitaev (SYK) model which slightly…
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Taxonomy
TopicsHistory and Theory of Mathematics
