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DESCRIPTION: Physics-based simulations are often used to drive product design. To extract meaningful information to support design decisions, model surrogates are often used to reduce execution times to allow a high number of parameter evaluations in the design space. Historically, these model surrogates only provide limited information through a few scaler KPIs. To retain comprehensive 3D simulation results while massively reducing execution time, this work presents a neural network approach towards 3D interactive design exploration.\n \n3D multiphysics-multiscale simulations (FEA analyses of structural statics, dynamics, manufacturing, packaging and safety; CFD analyses of compressible fluids) are used in a Design of Experiments (DOEs) to generate the parametric design data. This data is then processed and used to train neural networks as full 3D surrogates for fast and accurate predictions both transient physical responses and full 3D fields.\n
DTSTART:20231012T170000
DTEND:20231012T180000
DTSTAMP:20260415T005031Z
LOCATION:Online
PRIORITY:5
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SUMMARY;LANGUAGE=es-es:Tecnowebinars.com - :: Impacto brutal del Machine Learning en la Simulación 3D en diseño industrial. Por Dassault.
TRANSP:OPAQUE
UID:a63eab729ff7e0b7d31645805de1121c Tecnowebinars.com
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