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VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:3
UID:UW-Physics-Event-8150
DTSTART:20230301T190000Z
DTEND:20230301T203000Z
DTSTAMP:20260414T121420Z
LAST-MODIFIED:20230220T133638Z
LOCATION:Chamberlin 5280
SUMMARY:Machine Learning for String Compactifications\, Theory Seminar
  (High Energy/Cosmology)\, Anthony Ashmore\, U. Chicago
DESCRIPTION:The mysterious nature of Calabi-Yau metrics and hermitian 
 Yang-Mills connections has been a persistent challenge in mathematics 
 and theoretical physics for decades. These elusive geometric objects p
 lay a critical role in deriving semi-realistic models of particle phys
 ics from string theory. However\, with no explicit expressions for the
 m\, we are left unable to compute basic quantities in top-down string 
 models\, such as particle masses and couplings.
\nRecent breakthroughs
  in machine learning have opened up a new avenue for tackling this pro
 blem. In this seminar\, we will explore the potential of machine learn
 ing for computing these elusive objects. Starting with a review of the
 ir relationship to effective field theories\, we will then delve into 
 the latest progress in using machine learning to calculate Calabi-Yau 
 metrics and hermitian Yang-Mills connections numerically. Finally\, we
  will give examples of practical applications of this new data\, inclu
 ding a test of the so-called "swampland distance conjecture".
\n
URL:https://www.physics.wisc.edu/events/?id=8150
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