28 citations · 55 across the 4 of their papers we have counts for
4 papers
AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problems
Hyun-Sik Jeong, Hanse Kim, Keun-Young Kim +3
We apply physics-informed machine learning (PIML) to solve inverse problems in holography and classical mechanics, focusing on neural ordinary differential equations (Neural ODEs)…
Deep learning-based holography for T-linear resistivity
Byoungjoon Ahn, Hyun-Sik Jeong, Chang-Woo Ji +2
We employ deep learning within holographic duality to investigate -linear resistivity, a hallmark of strange metals. Utilizing Physics-Informed Neural Networks, we incorporate b…
Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim +1
We investigate the bulk reconstruction of AdS black hole spacetime emergent from quantum entanglement within a machine learning framework. Utilizing neural ordinary differential eq…
Deep learning bulk spacetime from boundary optical conductivity
Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim +1
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, ta…