Machine-Learning the Classification of Spacetimes
This work addresses a long-established classification problem in physics for researchers in general relativity, but it is incremental as it applies existing machine learning methods to a new domain.
The paper tackled the problem of classifying spacetimes in general relativity by applying machine learning techniques, specifically using a feed-forward neural network to achieve a high degree of success in modeling Petrov's classification.
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.