Search Results for author:
Found 6 papers, 3 papers with code
Decoding Nature with Nature's Tools: Heterotic Line Bundle Models of Particle Physics with Genetic Algorithms and Quantum Annealing
The string theory landscape may include a multitude of ultraviolet embeddings of the Standard Model, but identifying these has proven difficult due to the enormous number of available string compactifications.
Learning Size and Shape of Calabi-Yau Spaces
We present a new machine learning library for computing metrics of string compactification spaces.
Heterotic String Model Building with Monad Bundles and Reinforcement Learning
We use reinforcement learning as a means of constructing string compactifications with prescribed properties.
Machine Learning Calabi-Yau Four-folds
Hodge numbers of Calabi-Yau manifolds depend non-trivially on the underlying manifold data and they present an interesting challenge for machine learning.
Machine Learning String Standard Models
We study machine learning of phenomenologically relevant properties of string compactifications, which arise in the context of heterotic line bundle models.
A Comprehensive Scan for Heterotic SU(5) GUT models
Compactifications of heterotic theories on smooth Calabi-Yau manifolds remains one of the most promising approaches to string phenomenology.
High Energy Physics - Theory
