Digital twins are powerful virtual representations of physical systems, built using advanced computational models such as CFD (Computational Fluid Dynamics) and FEA (Finite Element Analysis). While highly accurate, these full-scale models often face challenges—they are expensive, demand expert users, and require heavy computational resources.
Reduced Order Models (ROMs) simplify these complexities by creating cost-effective, user-friendly models that maintain high physical accuracy. With ROMs, non-experts can easily interact with complex simulations and derive valuable insights. They are essential building blocks for physics-based digital twins but have been hindered by reliance on proprietary and expensive software.
Join us on this exclusive session on “ Building ROMs and Digital Twins using Python Libraries”, discover: