Abstract: A machine-learning method for learning a function for predicting a next feature in a feature tree. The method includes obtaining a dataset of examples each including a graph representing at least a part of a feature tree. The graph includes nodes each representing a feature and each labeled with a label of a set of labels each indicating a feature type of a predetermined set. The graph includes edges each connecting nodes and represents a parent-child relationship between the features represented by the nodes. The graph comprises ground truth data indicating a next feature according to one or more selected features each corresponding to a graph node. The method further includes training the function to take as input a graph representing at least a part of a feature tree and to output a prediction of one or more next features in the feature tree.

Abstract: A computer-implemented method for machine-learning a function. The method includes obtaining a dataset including 2D polyline profiles each representing respectively a portion of a manufacturing contour in a result of a structural optimization that represents a mechanical part, each 2D polyline profile being associated in the dataset with a respective primitive parametric curve class among a predetermined set of primitive parametric curve classes. The method further comprises training the function based on the dataset. The function is configured to take an input 2D polyline profile and to provide an output primitive parametric curve class. Such a method forms an improved solution for processing a result of a structural optimization that represents a mechanical part.