AdS/Deep-Learning made easy: simple examples

Abstract

Deep learning has been widely and actively used in various research areas. Recently, in the gauge/gravity duality, a new deep learning technique so-called the AdS/Deep-Learning (DL) has been proposed [1, 2]. The goal of this paper is to describe the essence of the AdS/DL in the simplest possible setups, for those who want to apply it to the subject of emergent spacetime as a neural network. For prototypical examples, we choose simple classical mechanics problems. This method is a little different from standard deep learning techniques in the sense that not only do we have the right final answers but also obtain a physical understanding of learning parameters.

17 pages, 12 figures

References in corpus (2)

• Deep Learning and AdS/CFT
• Deep Learning and Holographic QCD

Cited by in corpus (8)

• Deriving dilaton potential in improved holographic QCD from meson spectrum
• Deep learning bulk spacetime from boundary optical conductivity
• Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy
• Bulk reconstruction of metrics inside black holes by complexity
• Gravitational Duals from Equations of State
• Dual Geometry of Entanglement Entropy via Deep Learning
• Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE
• Deep learning-based holography for T-linear resistivity