From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook
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Abstract (arXiv)
We review the recent programme of using machine-learning to explore the landscape of mathematical problems. With this paradigm as a model for human intuition - complementary to and in contrast with the more formalistic approach of automated theorem proving - we highlight some experiments on how AI helps with conjecture formulation, pattern recognition and computation.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 63 references that are in the map. First posted on arXiv 12 Feb 2022.
Builds on
14 of 63 references.
References · 14
- 2020Learning to Unknotcited by 40
- 2020Detecting Symmetries with Neural Networkscited by 33
- 2020Neural Network Approximations for Calabi-Yau Metricscited by 32
- 2021Learning Size and Shape of Calabi-Yau Spacescited by 28
- 2021Machine-Learning Mathematical Structurescited by 25
- 2018Machine Learning CICY Threefoldscited by 22
- 2017Deep-Learning the Landscapecited by 21
- 2020Learning selection strategies in Buchberger's algorithmcited by 20
- 2018The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learningcited by 19
- 2019Learning Algebraic Structures: Preliminary Investigationscited by 15
- 2020Machine Learning Lie Structures & Applications to Physicscited by 13
- 2020Graph Laplacians, Riemannian Manifolds and their Machine-Learningcited by 12
2 more
- 2021Learning knot invariants across dimensionscited by 12
- 2021Machine Learning Kreuzer--Skarke Calabi--Yau Threefoldscited by 8
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Citing papers per year: 2022: 2.
2 papers.