Search for Z/2 eigenfunctions on the sphere using machine learning

Fuente: arXiv
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Autori principali: Haydys, Andriy, Salm, Willem Adriaan
Natura: Preprint
Pubblicazione: 2025
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author Haydys, Andriy
Salm, Willem Adriaan
author_facet Haydys, Andriy
Salm, Willem Adriaan
contents We use machine learning to search for examples of Z/2 eigenfunctions on the 2-sphere. For this we created a multivalued version of a feedforward deep neural network, and we implemented it using the JAX library. We found Z/2 eigenfunctions for three cases: In the first two cases we fixed the branch points at the vertices of a tetrahedron and at a cube respectively. In a third case, we allowed the AI to move the branch points around and, in the end, it positioned the branch points at the vertices of a squashed tetrahedron.
format Preprint
id arxiv_https___arxiv_org_abs_2507_13122
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Search for Z/2 eigenfunctions on the sphere using machine learning
Haydys, Andriy
Salm, Willem Adriaan
Differential Geometry
Machine Learning
Numerical Analysis
53-08, 53C99
We use machine learning to search for examples of Z/2 eigenfunctions on the 2-sphere. For this we created a multivalued version of a feedforward deep neural network, and we implemented it using the JAX library. We found Z/2 eigenfunctions for three cases: In the first two cases we fixed the branch points at the vertices of a tetrahedron and at a cube respectively. In a third case, we allowed the AI to move the branch points around and, in the end, it positioned the branch points at the vertices of a squashed tetrahedron.
title Search for Z/2 eigenfunctions on the sphere using machine learning
topic Differential Geometry
Machine Learning
Numerical Analysis
53-08, 53C99
url https://arxiv.org/abs/2507.13122