  In this paper, we propose a sensitivity-free and multi-objective structural
design methodology called data-driven topology design. It is schemed to obtain
high-performance material distributions from initially given material
distributions in a given design domain. Its basic idea is to iterate the
following processes: (i) selecting material distributions from a dataset of
material distributions according to eliteness, (ii) generating new material
distributions using a deep generative model trained with the selected elite
material distributions, and (iii) merging the generated material distributions
with the dataset. Because of the nature of a deep generative model, the
generated material distributions are diverse and inherit features of the
training data, that is, the elite material distributions. Therefore, it is
expected that some of the generated material distributions are superior to the
current elite material distributions, and by merging the generated material
distributions with the dataset, the performances of the newly selected elite
material distributions are improved. The performances are further improved by
iterating the above processes. The usefulness of data-driven topology design is
demonstrated through numerical examples.
