533 citations · 1.7k across the 63 of their papers we have counts for
4 papers · 1 filter
Moduli-dependent Calabi-Yau and SU(3)-structure metrics from Machine Learning
Lara B. Anderson, Mathis Gerdes, James Gray +3
We use machine learning to approximate Calabi-Yau and SU(3)-structure metrics, including for the first time complex structure moduli dependence. Our new methods furthermore improve…
Learning to Unknot
Sergei Gukov, James Halverson, Fabian Ruehle +1
We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots. We study the UNKNOT problem of determining whethe…
Machine Learning and Algebraic Approaches towards Complete Matter Spectra in 4d F-theory
Martin Bies, Mirjam Cvetic, Ron Donagi +3
Motivated by engineering vector-like (Higgs) pairs in the spectrum of 4d F-theory compactifications, we combine machine learning and algebraic geometry techniques to analyze line b…
Non-Simply-Connected Symmetries in 6D SCFTs
Markus Dierigl, Paul-Konstantin Oehlmann, Fabian Ruehle
Six-dimensional N=(1,0) superconformal field theories can be engineered geometrically via F-theory on elliptically-fibered Calabi-Yau 3-folds. We include torsional sections in the…