Artificial Neural Network in Cosmic Landscape
This addresses computational bottlenecks in cosmology for researchers, but it is incremental as it applies an existing method to a new domain.
The authors tackled the problem of exponential complexity in simulating cosmic inflationary landscapes with many fields by applying artificial neural networks, demonstrating feasibility through a toy model with multiple light fields.
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.