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Artificial neural network in cosmic landscape. (English) Zbl 1383.85016
Summary: 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.

MSC:
85A40 Cosmology
83C45 Quantization of the gravitational field
83F05 Cosmology
62P35 Applications of statistics to physics
83D05 Relativistic gravitational theories other than Einstein’s, including asymmetric field theories
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