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The size of the training data set significantly impacts the quality of data-driven models. However, creating the data set is also one of the most significant expenses in the model development process. This is especially true for metamodels designed for structural optimization. Second, the data must be generated using computationally intensive finite element simulations. Therefore, it is essential to understand how the amount of available data influences the balance between computing costs and expected model quality to get a good trade-off. This relationship will be analyzed for a metamodel approach based on an autoencoder applied to a forming process. By applying appropriate methods of instance selection, also known as subset selection, the required data can be strongly reduced, and computational costs can be minimized.

Efficiently determining the effect of data set size on autoencoder-based metamodels for structural design optimization