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Talk30 min

Machine-learning in Calabi-Yau geometry

Magdalena Larfors · CERN

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About

Calabi Yau (CY) manifolds are used ubiquitously in string theory. These spaces are central both to string phenomenology and formal developments. While CY manifolds can be constructed in large numbers, and much is known about their topological features, their geometry is still partly enigmatic. In particular, we lack an analytical expression for Ricci-flat metrics on CY n-folds for n>2. In this talk, I will review recent work on obtaining numerical approximations of CY metrics using machine learning, and how such metrics may be used string theory research..

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The talk

  1. Machine-learning in Calabi-Yau geometry 2024-06-05 · 30 min · CERN's player

Credits and licence

Institution
CERN
Department
Conferences, Workshops & Schools
Speaker
Magdalena Larfors
Year
2024
Language
English
Subjects
Mathematics, Computer science
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
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