Machine-Learning the Classification of Spacetimes

TL;DR

This paper applies machine learning techniques, specifically neural networks and data visualization, to classify and analyze different types of spacetimes in general relativity, offering a novel computational approach.

Contribution

It introduces a machine learning framework for Petrov classification of spacetimes, demonstrating high accuracy and new data analysis methods in this domain.

Findings

Neural networks can effectively classify Petrov types.

Dimensionality reduction reveals underlying patterns in spacetime data.

Machine learning enhances understanding of spacetime structures.

Abstract

On the long-established classification problems in general relativity we take a novel perspective by adopting fruitful techniques from machine learning and modern data-science. In particular, we model Petrov's classification of spacetimes, and show that a feed-forward neural network can achieve high degree of success. We also show how data visualization techniques with dimensionality reduction can help analyze the underlying patterns in the structure of the different types of spacetimes.