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SUMMARY:Calabi-Yau metrics through Grassmannian learning and Donaldson's a
 lgorithm - Oisin Kim\, CST
DTSTART:20241202T120000Z
DTEND:20241202T123000Z
UID:TALK224872@talks.cam.ac.uk
CONTACT:Sam Nallaperuma-Herzberg
DESCRIPTION:Motivated by recent progress in the problem of numerical Kähl
 er metrics\, we survey machine learning techniques in this area\, discussi
 ng both advantages and drawbacks. We then present a novel approach to obta
 ining Ricci-flat approximations to Kähler metrics\, applying machine lear
 ning within a `principled' framework\, inspired by the algebraic ansatz of
  Donaldson.
LOCATION:FW11\, Willam Gates building (Department of Computer Science and 
 Technology)
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