Machine Learning G2 Geometry
CMSA EVENTS: CMSA NEW TECHNOLOGIES IN MATHEMATICS
Compact Ricci-flat Calabi-Yau and holonomy G2 manifolds appear in string and M-theory respectively as descriptions of the extra spatial dimensions that arise in the theories. Since 2017 machine-learning techniques have been applied extensively to study Calabi-Yau manifolds but until 2024 no similar work had been carried out on holonomy G2 manifolds. In this talk, I will firstly show how topological properties of these manifolds can be learnt using neural networks. I will then discuss how one could try to numerically learn metrics on compact holonomy G2 manifolds using machine-learning and why these approximations would be useful in M-theory.
In person or on Zoom:
https://harvard.zoom.us/j/92220006185?pwd=V3mrb4cNSbgRXtNJtRJkTvWFVhmbI5.1
Password: cmsa