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SUMMARY:Machine-learning Sasakian and G2 topology on contact Calabi-Yau 7-
 manifolds - Daattavya Aggarwal\, Computer Laboratory
DTSTART:20231010T150000Z
DTEND:20231010T160000Z
UID:TALK207073@talks.cam.ac.uk
CONTACT:Challenger Mishra
DESCRIPTION:We propose a machine-learning approach to study topological qu
 antities related to the Sasakian and G2-geometries of contact Calabi-Yau 7
 -manifolds. Specifically\, we compute datasets for certain Sasakian Hodge 
 numbers and for the Crowley-Nördstrom invariant of the natural G2-structu
 re of the 7-dimensional link of a weighted projective Calabi-Yau 3-fold hy
 persurface singularity\, for each of the 7555 possible ℙ4(w) projective 
 spaces. These topological quantities are then machine learnt with high acc
 uracy\, along with properties of the respective Gröbner basis\, leading t
 o a vast improvement in computation speeds which may be of independent int
 erest. We observe promising results in machine learning the Sasakian Hodge
  numbers from the ℙ4(w) weights alone\, using both neural networks and a
  symbolic regressor which achieve R2 scores of 0.969 and 0.993 respectivel
 y\, inducing novel conjectures to be raised.\n\n\nhttps://arxiv.org/abs/23
 10.03064
LOCATION:Computer Lab\, SS03
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