mlsanityData to 7 Oct 2026

Machine Learning Algebraic Geometry for Physics

arXiv 2204.10334 ↗First posted 21 Apr 2022hep-th math.AG stat.MLcited by 3

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Abstract (arXiv)

We review some recent applications of machine learning to algebraic geometry and physics. Since problems in algebraic geometry can typically be reformulated as mappings between tensors, this makes them particularly amenable to supervised learning. Additionally, unsupervised methods can provide insight into the structure of such geometrical data. At the heart of this programme is the question of how geometry can be machine learned, and indeed how AI helps one to do mathematics. This is a chapter contribution to the book Machine learning and Algebraic Geometry, edited by A. Kasprzyk et al.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 100 references that are in the map. First posted on arXiv 21 Apr 2022.

Builds on

20 of 100 references.

References · 20

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8 more
  1. 2020Graph Laplacians, Riemannian Manifolds and their Machine-Learningcited by 12
  2. 2021Neurons on Amoebaecited by 11
  3. 2021Deep multi-task mining Calabi-Yau four-foldscited by 11
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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 1.

3 papers.

  1. 2023Machine Learning Clifford invariants of ADE Coxeter elementscited by 6
  2. 2025Machine Learning meets Algebraic Combinatorics: A Suite of Datasets Capturing Research-level Conjecturing Ability in Pure Mathematicscited by 5
  3. 2023Calabi-Yau Four/Five/Six-folds as mathbbPnw Hypersurfaces: Machine Learning, Approximation, and Generationcited by 0