Data-driven topology design using a deep generative model

In this paper, we propose a structural design methodology called \textit{data-driven topology design}, which aims to obtain high-performance material distributions for a multi-objective optimization problem from the initially given material distributions in a given design domain. Its basic idea is iterating the following processes: (i) selecting the material distributions from a dataset according to Pareto optimality, (ii) generating new material distributions using a deep generative model with the selected material distributions as the training data, and (iii) integrating the generated material distributions into the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inheriting features of the training data, which are material distributions on the Pareto front at that specific point. Therefore, it is expected that some of the generated material distributions are superior to the training data, whereas some are inferior, and the Pareto front is improved by integrating the generated material distributions into the dataset. The Pareto front is further improved by iterating the above processes. Data-driven topology design is used to enhance a support system for determining appropriate formulations of topology optimization problems, and its usefulness is demonstrated through numerical examples.