Machine-Learning the Classification of Spacetimes
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 5 Jan 2022.
Builds on
9 of 33 references.
References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2020Detecting Symmetries with Neural Networkscited by 34
- 2021Machine-learning hidden symmetriescited by 31
- 2021Machine-Learning Mathematical Structurescited by 25
- 2018The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learningcited by 19
- 2019Learning Algebraic Structures: Preliminary Investigationscited by 15
- 2020Graph Laplacians, Riemannian Manifolds and their Machine-Learningcited by 12
- 2019Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyercited by 8
- 2021The Autodidactic Universecited by 7
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Citing papers per year: 2024: 1.
1 paper.