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Artificial neural network in cosmic landscape

A preprint version of the article is available at arXiv.


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.


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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).

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