Machine-learning the classification of spacetimes
He, Y. 
ORCID: 0000-0002-0787-8380 & Pérez Ipiña, J. M. (2022).
Machine-learning the classification of spacetimes.
Physics Letters B, 832,
article number 137213.
doi: 10.1016/j.physletb.2022.137213
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.
| Publication Type: | Article |
|---|---|
| Additional Information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QC Physics |
| Departments: | School of Science & Technology > Mathematics |
| SWORD Depositor: |
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Official URL: https://doi.org/10.1016/j.physletb.2022.137213
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