Talk page

Title:
Line Bundle Cohomology and Machine Learning

Speaker:
Andre Lukas

Abstract:
Explicit knowledge of bundle cohomology, in particular for line bundles, is essential for many classes of string compactifications. I will describe algorithmic techniques to compute bundle cohomology and explain how these lead to explicit formulae for the dimensions of line bundle cohomology an Calabi-Yau three-folds as well as on certain surfaces. In the second half of the talk I discuss how techniques from machine learning, which have recently been introduced into string theory, can be applied to the problem of computing line bundle cohomology.

Link:
http://scgp.stonybrook.edu/video_portal/video.php?id=3952

Workshop:
Simons- Geometry and Physics of Hitchin Systems: January 22 - June 21, 2019