Intelligent Explorations of the String Theory Landscape
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 10 of its 158 references that are in the map. First posted on arXiv 17 Apr 2022.
Builds on
10 of 158 references.
References · 10
- 2020Learning to Unknotcited by 40
- 2020Neural Network Approximations for Calabi-Yau Metricscited by 32
- 2021Learning Size and Shape of Calabi-Yau Spacescited by 28
- 2019Machine learning Calabi-Yau metricscited by 23
- 2021Machine Learning Calabi-Yau Hypersurfacescited by 15
- 2020Explore and Exploit with Heterotic Line Bundle Modelscited by 11
- 2019Searching the Landscape of Flux Vacua with Genetic Algorithmscited by 10
- 2021Heterotic String Model Building with Monad Bundles and Reinforcement Learningcited by 10
- 2020Machine Learning String Standard Modelscited by 7
- 2021Breeding realistic D-brane modelscited by 4
Built on by
Citing papers per year: 2023: 1.
1 paper.