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.
¿Le gustaría hacer webinars o eventos online con nosotros?
|