BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Neural Network Approximations for Calabi-Yau Metrics - Challenger 
 Mishra
DTSTART:20210222T170000Z
DTEND:20210222T173000Z
UID:TALK157639@talks.cam.ac.uk
CONTACT:Bingqing Cheng
DESCRIPTION:String theory is the only known consistent theory of quantum g
 ravity. The extra-dimensional part of space posited by string theory is of
 ten described by complex geometries called Calabi--Yau manifolds. In order
  for string theory to make predictions for masses of fundamental particles
 \, such as electrons\, we require knowledge of a special Riemannian metric
  over Calabi--Yau threefolds. Such metrics\, known as Ricci flat metrics\,
  are solutions to partial differential equations that are notoriously diff
 icult to solve. In fact\, no analytic solution is known for metrics of Cal
 abi--Yau threefolds.\nWe employ techniques from machine learning to deduce
  numerical flat metrics for certain phenomenologically important Calabi--Y
 au geometries\, namely\, the Fermat quintic\, the Dwork quintic\, and the 
 Tian-Yau manifold. We show that measures that assess the Ricci flatness of
  the geometry decrease after training by three orders of magnitude. This i
 s corroborated on the validation set\, where the improvement is more modes
 t. Finally\, we demonstrate that discrete symmetries of manifolds can be l
 earned in the process of learning the metric.
LOCATION:virtual ZOOM meeting ID: 263 591 6003\, Passcode: 000042\, https:
 //us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09
END:VEVENT
END:VCALENDAR