CAE Engineers: Mastering AI for a Smarter Future

This article highlights a crucial synergy: the need for CAE engineers to guide and validate AI models, ensuring they align with physical realities. It emphasizes how engineers can use their domain expertise to overcome AI's limitations, especially regarding physics, and drive innovation in the field. The final checklist is a brilliant summary for practical application.

• •AI models can be improved by making them "weaker" using techniques like Dropout to enhance their robustness.
• •Increasing the quantity and quality of data, especially focusing on areas with strong non-linearity, is critical for improved accuracy.
• •Integrating physical laws directly into AI models (PINNs) provides a powerful way to enhance reliability and prevent incorrect results.