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SUMMARY:Physics-informed neural network solves minimal surfaces in curved 
 spacetime
DTSTART:20260618T070000Z
DTEND:20260618T083000Z
DTSTAMP:20260531T204700Z
UID:indico-event-414@indico.kias.re.kr
DESCRIPTION:Speakers: Norihiro Tanahashi (Kyoto University)\n\nTitle: Phys
 ics-informed neural network solves minimal surfaces in curved spacetime\nS
 peaker: Norihiro Tanahashi\nAbstract: We develop a flexible framework base
 d on physics-informed neural networks (PINNs) to solve boundary value prob
 lems for minimal surfaces in curved spacetimes\, with particular emphasis 
 on singularities and moving boundaries. By encoding the underlying physica
 l laws into the loss function and designing network architectures that inc
 orporate singular behavior and dynamic boundaries\, our approach enables r
 obust\, accurate solutions to both ordinary and partial differential equat
 ions with complex boundary conditions. We demonstrate the versatility of t
 his framework by applying it to minimal surface problems in anti-de Sitter
  (AdS) spacetime\, including examples relevant to the AdS/CFT corresponden
 ce (e.g.\, Wilson loops and gluon scattering amplitudes).\nJoin Zoom Meeti
 ng\nhttps://kias-re-kr.zoom.us/j/82231139096?pwd=x1rbJyCEBNU3QzP7DPn9Vdzbp
 cOafI.1\nMeeting ID: 822 3113 9096\nPasscode: 178946\n\nhttps://indico.kia
 s.re.kr/event/414/
LOCATION:Room 1423 (KIAS)
URL:https://indico.kias.re.kr/event/414/
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