AdS/Deep-Learning made easy: simple examples

TL;DR

This paper simplifies the understanding of AdS/Deep-Learning by demonstrating its application to basic classical mechanics problems, providing both solutions and physical insights into the learning process.

Contribution

It presents a straightforward approach to applying AdS/Deep-Learning to simple systems, making the method accessible for emergent spacetime research.

Findings

Successful application to classical mechanics problems

Provides physical interpretation of learning parameters

Simplifies the conceptual understanding of AdS/Deep-Learning

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.