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DTSTART:19700308T020000
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DTSTAMP:20250822T115808Z
LOCATION:Room 5.0B56
DTSTART;TZID=Europe/Stockholm:20250617T153000
DTEND;TZID=Europe/Stockholm:20250617T160000
UID:submissions.pasc-conference.org_PASC25_sess125_msa278@linklings.com
SUMMARY:Assessing the state of the art for AI surrogate methods applied to
  external automotive aerodynamics – successes and failures
DESCRIPTION:Neil Ashton (NVIDIA Inc.)\n\nIn this talk, we will focus on di
 scussing recent work to assess the capability of AI surrogate models in th
 e prediction of automotive aerodynamics. In particular, the talk will focu
 s on both dataset generation (specifically efforts behind generating the A
 hmedML, WindsorML and DrivAerML open-source datasets) as well as results f
 rom both GNN and neural operator approaches for surface, volume and integr
 al quantities. The focus will be on bringing to light successes but also f
 ailures and challenges in current methods that can hopefully motivate furt
 her development by the community.\n\nDomain: Engineering, Computational Me
 thods and Applied Mathematics\n\nSession Chair: Neil Ashton (NVIDIA Inc.)\
 n\n
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