- Typically, the quality of AI models is highly dependent on the amount and quality of the available training data. While for many applications of AI several million training datasets are available in accessible databases and can be easily extended, the generation of training data for surrogate models of physics simulations is computationally demanding. To identify the necessary amount of training data for a sufficient good AI model, we are evaluating the performance of a surrogate model for a thermomechanical production process with different sizes of artificially generated training data.
