Boosting Surrogate Models: The Power of Physics-Based AI

This article highlights an innovative approach to enhance the accuracy of surrogate models in CAE simulations by integrating a deep understanding of physics. It emphasizes the importance of incorporating physical principles, such as dimensionless numbers and conservation laws, to create more robust and reliable AI-driven solutions.

• •Integrating physical insights dramatically reduces data requirements and boosts accuracy.
• •Using dimensionless numbers like Reynolds number helps to generalize models.
• •Validating models with physical consistency checks (e.g., conservation laws) ensures reliability.