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.

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Cited by in corpus (15)

• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• Deep Learning and AdS/CFT
• Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
• AdS/CFT as a deep Boltzmann machine
• Machine learning Calabi-Yau metrics
• Classification of Standard-like Heterotic-String Vacua
• Getting CICY High
• The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning
• Estimating Calabi-Yau Hypersurface and Triangulation Counts with Equation Learners
• Manyfield Inflation in Random Potentials
• Statistical Predictions in String Theory and Deep Generative Models
• A Triumvirate of AI Driven Theoretical Discovery
• Learning to Inflate
• Detection of Dipole Modulation in CMB Temperature Anisotropy Maps from WMAP and Planck using Artificial Intelligence
• Universes as Big Data