BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Rak-Kyeong Seong (UNIST) DTSTART:20220518T070000Z DTEND:20220518T080000Z DTSTAMP:20260423T023941Z UID:NMTP2022/11 DESCRIPTION:Title: Machine Learning Calabi-Yau Volumes\nby Rak-Kyeong Seong (UNIST) as part of Numerical Methods in Theoretical Physics\n\n\nAbstract\nThe talk w ill give an overview of our work from 2017 which introduced machine learni ng techniques in string theory. This work made use of standard machine lea rning techniques\, including convolutional neural networks (CNN)\, in orde r to find new formulas for the minimum volume of Sasaki-Einstein manifolds corresponding to toric Calabi-Yau 3-folds. These geometries\, by the AdS/ CFT correspondence\, relate to a large class of $4d N=1$ supersymmetric ga uge theories. The R-charges of the dual gauge theories are known to be rel ated to the minimum volumes of the corresponding Sasaki-Einstein manifolds . In this talk\, we will review the process of volume minimization and giv e a short overview on ongoing work.\n LOCATION:https://researchseminars.org/talk/NMTP2022/11/ END:VEVENT END:VCALENDAR