# Intelligent Explorations of the String Theory Landscape

arXiv: https://arxiv.org/abs/2204.08073 · HTML: https://mlsanity.com/paper/2204.08073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 158 references that are in the map.
- First posted on arXiv 17 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Learning to Unknot](https://mlsanity.com/paper/2010.16263/) (2020)
- [Neural Network Approximations for Calabi-Yau Metrics](https://mlsanity.com/paper/2012.15821/) (2020)
- [Learning Size and Shape of Calabi-Yau Spaces](https://mlsanity.com/paper/2111.01436/) (2021)
- [Machine learning Calabi-Yau metrics](https://mlsanity.com/paper/1910.08605/) (2019)
- [Machine Learning Calabi-Yau Hypersurfaces](https://mlsanity.com/paper/2112.06350/) (2021)
- [Explore and Exploit with Heterotic Line Bundle Models](https://mlsanity.com/paper/2003.04817/) (2020)
- [Searching the Landscape of Flux Vacua with Genetic Algorithms](https://mlsanity.com/paper/1907.10072/) (2019)
- [Heterotic String Model Building with Monad Bundles and Reinforcement Learning](https://mlsanity.com/paper/2108.07316/) (2021)
- [Machine Learning String Standard Models](https://mlsanity.com/paper/2003.13339/) (2020)
- [Breeding realistic D-brane models](https://mlsanity.com/paper/2112.08391/) (2021)

## Built on by (most cited first, 1 of 1)

- [Decoding Nature with Nature's Tools: Heterotic Line Bundle Models of Particle Physics with Genetic Algorithms and Quantum Annealing](https://mlsanity.com/paper/2306.03147/) (2023)

## Abstract (arXiv)

The goal of identifying the Standard Model of particle physics and its extensions within string theory has been one of the principal driving forces in string phenomenology. Recently, the incorporation of artificial intelligence in string theory and certain theoretical advancements have brought to light unexpected solutions to mathematical hurdles that have so far hindered progress in this direction. In this review we focus on model building efforts in the context of the E8× E8 heterotic string compactified on smooth Calabi-Yau threefolds and discuss several areas in which machine learning is expected to make a difference.