Speaker: Cheng Ran (Shanghai University)
Time: 2025-09-18 15:00:00
Venue: R1510
Abstracts:
We introduce a data-driven method for holographic bulk reconstruction that does not rely on pre-defined asymptotic behavior. The framework is founded on the holographic Wilsonian renormalization group (HWRG), which is used to derive a radial flow equation for a bulk response function from the Klein-Gordon equation. This first-order differential equation is then transformed into a Neural Ordinary Differential Equation (Neural ODE), a type of deep neural network designed for modeling continuous dynamics. 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 deviates from the conformal limit.
Introduction to the speaker: Cheng Ran is a Ph.D. student in the Department of Physics at Shanghai University. His research interests lie in holographic bulk reconstruction from boundary data, and his current focus is on developing data-driven algorithms to reconstruct bulk geometries using Green’s functions from the boundary field theory
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