Digital twins are virtual representations of physical objects, leveraging computational models like CFD and FEA. While these full-scale models are powerful, they come with significant challenges—they are costly, require expert users, and consume extensive 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:
- Strategies for building practical and efficient digital twins.
- Using advanced methods like Gappy POD for developing parametric ROMs.
- Leveraging Python libraries to create and implement ROMs effectively.
Featured Presenter
Dr. Mothivel Mummudi
Global Industry Director
Prerak Dongaonkar
Product Owner & Support Manager
