When investigating Machine Learning approaches for engineering design cycles, the first question of the engineering team, most of the time, is regarding the number of simulations needed to train the model. There is not a single answer, as this number greatly varies from one use-case to another. It is influenced by numerous aspects however, the main factor is the design space to be explored by the predictive model.
From our experience at Neural Concept, we made the following graph giving some more insights on the size of the dataset required to start using Machine Learning as predictive model for the next engineering design cycles.
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