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Title: Artificial neural network in cosmic landscape

Abstract

In this study we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.

Authors:
 [1]
  1. California Inst. of Technology (CalTech), Pasadena, CA (United States)
Publication Date:
Research Org.:
California Inst. of Technology, Pasadena, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1501472
Grant/Contract Number:  
SC0011632
Resource Type:
Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2017; Journal Issue: 12; Journal ID: ISSN 1029-8479
Publisher:
Springer Berlin
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 79 ASTRONOMY AND ASTROPHYSICS; Cosmology of Theories beyond the SM; Models of Quantum Gravity; Random Systems

Citation Formats

Liu, Junyu. Artificial neural network in cosmic landscape. United States: N. p., 2017. Web. doi:10.1007/jhep12(2017)149.
Liu, Junyu. Artificial neural network in cosmic landscape. United States. doi:10.1007/jhep12(2017)149.
Liu, Junyu. Thu . "Artificial neural network in cosmic landscape". United States. doi:10.1007/jhep12(2017)149. https://www.osti.gov/servlets/purl/1501472.
@article{osti_1501472,
title = {Artificial neural network in cosmic landscape},
author = {Liu, Junyu},
abstractNote = {In this study we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.},
doi = {10.1007/jhep12(2017)149},
journal = {Journal of High Energy Physics (Online)},
number = 12,
volume = 2017,
place = {United States},
year = {2017},
month = {12}
}

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