How Many Design Experiments Does A Machine Learning Predictor Actually Need?

Machine learning prediction in CAE has a clear proposition: Train a predictor on solved design experiments, then evaluate new designs in seconds instead of hours . The proposition is only as good as the training dataset, and the dataset is the expensive part. Every design experiment is a full, explicit crash run. So the first real engineering decision in an ML workflow is how many experiments to commit to, and it is usually made on instinct. Four crash studies show the number of runs an ML predi