Identifying Topological Prototypes using Deep Point Cloud Autoencoder Networks Data mining of engineering designs generated by topology optimization methods is a challenging task. A topology optimization method maximizes one or more performance objectives by redistributing the material in a design space for a given set of boundary conditions and constraints. The performance objective could be stiffness and the constraint could be the allowed mass fraction in the design space. If the constraints are not too restrictive or unknown at the initial stages of the design process, multiple feasible designs are possible and are generally generated to inspire new design ideas. Since a designer cannot manually review all the designs, one needs to select a few representative and interesting topologies based on performance or geometric features. In this paper, we propose a method to group designs with similar geometric features which are extracted from a point cloud representation of the geometry using a deep autoencoder network. The point cloud representation is generated by sampling points on the surface of geometry. The extracted features  called latent code  can be used to cluster topologies and identify prototypes in each cluster. The proposed method could be used on designs generated by topology optimization and other design generation methods. To evaluate the method, we use it on complex trusslike topology datasets with prespecified design types which have been recognized by the proposed method with high precision and recall. Also, the prototypes of the different categories are identified.

Representation Learning via Parallel Subset Reconstruction for 3D Point Cloud Generation Threedimensional (3D) point cloud processing has attracted a great deal of attention in computer vision, robotics, and the machine learning community because of significant progress in deep neural networks on 3D data. Another trend in the community is learning of generative models based on generative adversarial networks. In this paper, we propose a framework for 3D point cloud generation based on a combination of autoencoders and generative adversarial networks. The framework first trains autoencoders to learn latent representations, and then trains generative adversarial networks in the learned latent space. We focus on improving the training method for autoencoders in order to generate 3D point clouds with higher fidelity and coverage. We add parallel subdecoders that reconstruct subsets of the input point cloud. In order to construct these subsets, we introduce a point sampling algorithm that imposes a method to sample spatially localized point sets. These local subsets are utilized to measure local reconstruction losses. We train autoencoders to learn an effective latent representation for both global and local shape reconstruction based on the multitask learning approach. Furthermore, we add global and local adversarial losses to generate more plausible point clouds. Quantitative and qualitative evaluations demonstrate that the proposed method outperforms stateoftheart method on the task of 3D point cloud generation.