Structural optimization through generative adversarial networks

Abstract

The finite element method (FEM) is a well known approach to solve partial differential equations. It has important applications in structural engineering, such as in topology optimization (TO). TO involves, at each iteration, the solution of structural problems via FEM, which can add up to a high computational cost. Therefore, a line of research to accelerate TO emerged over the years focusing on machine learning (ML) approaches. Particularly, Artificial Neural Networks (ANNs) have been proposed to significantly speed-up the process by eliminating the iterative algorithm, which is intrinsic to TO. Since ANN is a supervised ML method, first a dataset is generated, containing finite element analysis (FEA) inputs, volume fraction, postprocessing, and final topologies. Then, with the Wasserstein Generative Adversarial Networks (WGANs) is trained on this dataset to map fields of physical quantities, such as the von Mises stress, to the final optimized structure. The final designs obtained via ML are quantitatively analyzed according to the metrics.

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