Artificial Neural Network in Cosmic Landscape

TL;DR

This paper explores using artificial neural networks to efficiently generate and analyze the inflationary landscape in cosmology, addressing computational challenges in high-dimensional models.

Contribution

It introduces a neural network approach to model the cosmic landscape, offering a scalable alternative to traditional exponential complexity simulations.

Findings

Neural networks can approximate the inflationary landscape effectively.

The approach reduces computational complexity for models with many fields.

Numerical toy model demonstrates practical application of the method.

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.