# Distinguishing Elliptic Fibrations with AI

## TL;DR

This paper demonstrates that machine learning, specifically neural networks, can efficiently distinguish elliptic fibrations among Calabi-Yau manifolds, outperforming traditional algebraic methods and aiding research in string theory and algebraic geometry.

## Contribution

The study introduces a neural network approach to identify elliptic fibrations in Calabi-Yau manifolds, showing superior efficiency over classical techniques.

## Key findings

- Neural networks can accurately classify elliptic fibrations.
- Machine learning outperforms traditional algebraic methods.
- Results are applicable in F-theory and algebraic geometry.

## Abstract

We use the latest techniques in machine-learning to study whether from the landscape of Calabi-Yau manifolds one can distinguish elliptically fibred ones. Using the dataset of complete intersections in products of projective spaces (CICY3 and CICY4, totalling about a million manifolds) as a concrete playground, we find that a relatively simple neural network with forward-feeding multi-layers can very efficiently distinguish the elliptic fibrations, much more so than using the traditional methods of manipulating the defining equations. We cross-check with control cases to ensure that the AI is not randomly guessing and is indeed identifying an inherent structure. Our result should prove useful in F-theory and string model building as well as in pure algebraic geometry.