A computer-implemented method of machine-learning, the method comprising: - obtaining a training dataset of B-rep graphs, each B-rep graph representing a respective B-rep and comprising: o graph nodes each representing an edge, a face or a co-edge of the respective B-rep and being associated with one or more geometrical and/or topological features, where, for each node, the one or more geometrical and/or topological features form a feature vector attached to the node so that the one or more geometrical and/or topological features are also comprised in said each B-rep graph; and o graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge; and - learning, based on the training dataset, a Deep CAD neural network configured to take as input a B-rep graph and to output a topological signature of the B-rep represented by the input B-rep graph, the B-rep graph taken as input by the Deep CAD neural network comprising graph nodes each representing an edge, a face or a co-edge of the B-rep represented by the B-rep graph and being associated with one or more geometrical and/or topological features, where, for each node, the one or more geometrical and/or topological features form a feature vector attached to the node so that the one or more geometrical and/or topological features are also comprised in the B-rep graph taken as input by the Deep CAD neural network, the B-rep graph taken as input by the Deep CAD neural network also comprising graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge, the Deep CAD neural network being a deep neural network with a graph convolutional network architecture, wherein B-rep means boundary representation and CAD means computer-aided-design. - obtaining a training dataset of B-rep graphs, each B-rep graph representing a respective B-rep and comprising: o graph nodes each representing an edge, a face or a co-edge of the respective B-rep and being associated with one or more geometrical and/or topological features, where, for each node, the one or more geometrical and/or topological features form a feature vector attached to the node so that the one or more geometrical and/or topological features are also comprised in said each B-rep graph; and o graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge; and o graph nodes each representing an edge, a face or a co-edge of the respective B-rep and being associated with one or more geometrical and/or topological features, where, for each node, the one or more geometrical and/or topological features form a feature vector attached to the node so that the one or more geometrical and/or topological features are also comprised in said each B-rep graph; and o graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge; and - learning, based on the training dataset, a Deep CAD neural network configured to take as input a B-rep graph and to output a topological signature of the B-rep represented by the input B-rep graph, the B-rep graph taken as input by the Deep CAD neural network comprising graph nodes each representing an edge, a face or a co-edge of the B-rep represented by the B-rep graph and being associated with one or more geometrical and/or topological features, where, for each node, the one or more geometrical and/or topological features form a feature vector attached to the node so that the one or more geometrical and/or topological features are also comprised in the B-rep graph taken as input by the Deep CAD neural network, the B-rep graph taken as input by the Deep CAD neural network also comprising graph edges each between a respective first graph node representing a respective co-edge and a respective second graph node representing a face, an edge, an adjacent co-edge, or a mating co-edge associated with the respective co-edge, the Deep CAD neural network being a deep neural network with a graph convolutional network architecture,