AdS/Deep-Learning made easy:simple examples

查看全文 作　　者：Mugeon [1]Song;Maverick [1,2]S.H.Oh;Yongjun [1]Ahn;Keun-Young [1]Kima 机构地区：[1]Gwangju Institute of Science and Technology(GIST),Department of Physics and Photon Science,Gwangju,South Korea;[2]University of California–Merced,Department of Physics,Merced,CA,USA 出　　处：《Chinese Physics C》索引2021年第45卷第7期,共10页 基　　金：Supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science;ICT&Future Planning(NRF-2017R1A2B4004810,NRF-2021R1A2C1006791);the GIST Research Institute(GRI)grant funded by GIST in 2021。 摘　　要：Deep learning has been widely and actively used in various research areas.Recently,in gauge/gravity duality,a new deep learning technique called AdS/DL(Deep Learning)has been proposed.The goal of this paper is to explain the essence of AdS/DL in the simplest possible setups,without resorting to knowledge of gauge/gravity duality.This perspective will be useful for various physics problems:from the emergent spacetime as a neural network to classical mechanics problems.For prototypical examples,we choose simple classical mechanics problems.This method is slightly different from standard deep learning techniques in the sense that we not only have the right final answers but also obtain physical understanding of learning parameters. 关 键 词：gauge/gravity duality holographic principle machine learning