AdS/Deep-Learning made easy: simple examples
Classical Physics
2021-09-01 v2 Machine Learning
High Energy Physics - Theory
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.
Keywords
Cite
@article{arxiv.2011.13726,
title = {AdS/Deep-Learning made easy: simple examples},
author = {Mugeon Song and Maverick S. H. Oh and Yongjun Ahn and Keun-Young Kim},
journal= {arXiv preprint arXiv:2011.13726},
year = {2021}
}
Comments
17 pages, 12 figures