Artificial neural network in cosmic landscape

Abstract

In this paper 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.

A preprint version of the article is available at ArXiv.

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Correspondence to Junyu Liu.

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ArXiv ePrint: 1707.02800

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Liu, J. Artificial neural network in cosmic landscape. J. High Energ. Phys. 2017, 149 (2017). https://doi.org/10.1007/JHEP12(2017)149

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Keywords

  • Cosmology of Theories beyond the SM
  • Models of Quantum Gravity
  • Random Systems