CAE faces challenges including simulations taking too long to run, being too expensive due to HPC and licensing costs, and not always correlating well with test data due to unknown physics. AI-powered CAE aims to address these challenges by using machine learning models trained on past CAE and test data to provide faster predictive outputs, enabling more design optimization loops and democratizing CAE use without specialized skills.

8. AI POWERED CAE

9. Challenges faced by CAE
• In some instances, it takes too long to get a CAE result
• CAE can be perceived as being too expensive- HPC and license cost.
• All the physics may not be known leading to correlation issues with test data- Quality of decision making
impacted
• Variation Analysis, Design Optimization, DOE - Multiple loops of simulation which can be quite time
consuming
• Democratization of CAE- Needs specialized skills.
• Limited usage of past CAE/Test data (re-usability)
• Unable to meet real time predictive output requirements (especially for controls)

11. Model
Pre-
Process
Solve
Post-
Process
Report

14. From simulation validated by physical test to Design of
Experiment (DOE) fed AI models validated by CAE simulation or
physical test