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VERSION:2.0
PRODID:Linklings LLC
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TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:JST
DTSTART:18871231T000000
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BEGIN:VEVENT
DTSTAMP:20260817T171522Z
LOCATION:Lobby Gallery (1) & (2)\, G Block\, Level B1
DTSTART;TZID=Asia/Tokyo:20241205T130000
DTEND;TZID=Asia/Tokyo:20241205T140000
UID:siggraphasia_SIGGRAPH Asia 2024_sess200_pos_145@linklings.com
SUMMARY:Towards Accelerating Physics Informed Graph Neural Network for Flu
 id Simulation
DESCRIPTION:Yidi Wang (NVIDIA, Singapore Institute of Technology); Frank G
 uan, Malcolm Yoke Hean Low, and Daniel Wang (Singapore Institute of Techno
 logy); and Aik Beng Ng and Simon See (NVIDIA)\n\nWe introduce a pioneering
  Multi-GNN Processor Physics-Informed Graph Neural Network (PIGNN) approac
 h which reduced training time of PIGNN to a quarter while maintaining the 
 error rate.\n\nRegistration Category: Enhanced Access, Exhibit & Experienc
 e Access, Experience Hall Exhibitor, Full Access, Full Access Supporter, T
 rade Exhibitor\n\n
URL:https://asia.siggraph.org/2024/program/?id=pos_145&sess=sess195
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