[2006.00712v1] Neural ODE and Holographic QCDopen searchopen navigation menucontact arXivsubscribe to arXiv mailings

The neural ordinary differential equation (Neural ODE) is a novel machine learning architecture whose weights are smooth functions of the continuous depth. We apply the Neural ODE to holographic QCD by regarding the weight functions as a bulk metric, and train the machine with lattice QCD data of chiral condensate at finite temperature. The machine finds consistent bulk geometry at various values of temperature and discovers the emergent black hole horizon in the holographic bulk automatically. The holographic Wilson loops calculated with the emergent machine-learned bulk spacetime have consistent temperature dependence of confinement and Debye-screening behavior. In machine learning models with physically interpretable weights, the Neural ODE frees us from discretization artifact leading to difficult ingenuity of hyperparameters, and improves numerical accuracy to make the model more trustworthy.

1 mentions: @q9ac
Date: 2020/06/29 02:21

Referring Tweets

@q9ac Neural ODE=深さ&重み関数が連続になっている QCDのデータ→Neural ODE,重み関数を計量だと思うと,ブラックホールができるのが見えちゃうらしい. t.co/NgBzvbg3tW

Related Entries

Read more [2002.01099v1] A fluctuation theorem for Floquet quantum master equationscontact arXivarXiv Twitter
0 users, 1 mentions 2020/02/05 23:20
Read more [1906.04478v3] A short introduction to the Lindblad Master Equationcontact arXivarXiv Twitter
0 users, 1 mentions 2020/02/06 23:20
Read more [2002.01841v1] The Casimir force, causality and the Gurzhi modelcontact arXivarXiv Twitter
0 users, 1 mentions 2020/02/06 23:20
Read more [2005.05087v1] General relation between spatial coherence and absorptionopen searchopen navigation m...
0 users, 1 mentions 2020/06/01 02:21
Read more [2005.05973v1] Surface Green's functions and quasiparticle interference in Weyl semimetalsopen searc...
0 users, 1 mentions 2020/06/01 02:21