Analysis
This article highlights a crucial aspect often overlooked in AI: the importance of data quality. It offers valuable insights for CAE engineers transitioning to AI, emphasizing data preparation as critical as mesh quality in simulations. This practical guide equips engineers with essential knowledge to build effective surrogate models.
Key Takeaways
- •Data preparation is as crucial as mesh quality in CAE for AI models.
- •Normalization (scaling data) is a MUST-DO for accurate AI models.
- •Knowledge of Design of Experiments (DoE) is directly applicable and beneficial for AI.
Reference / Citation
View Original"When AI makes bad predictions, 90% of the time it's the 'data' that's bad."