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SUMMARY:A machine learning approach to duality in statistical physics - An
 drea Ferrari (University of Edinburgh and DESY)
DTSTART:20251111T140000Z
DTEND:20251111T150000Z
UID:TALK240736@talks.cam.ac.uk
CONTACT:Sven Krippendorf
DESCRIPTION:The notion of duality --the fact that a physical system enjoys
  inequivalent descriptions-- is a key driver of modern theoretical physics
 . In this talk I will formulate the task of duality discovery in statistic
 al physics as an optimisation problem that generalises the more standard o
 ne of fitting parameters in a Hamiltonian. I will show how a simple versio
 n of this problem can be solved to obtain an automated rediscovery of the 
 celebrated Kramers-Wannier duality for the 2d Ising model. If time will pe
 rmit\, I will conclude with some preliminary results concerning more compl
 icated models\, and discuss how the framework could be applied to investig
 ate unknown or poorly known dualities.
LOCATION:DAMTP\, MR11
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