Gradient-free optimizers allow for tackling problems regardless of the
smoothness or differentiability of their objective function, but they require
many more iterations to converge when compared to gradient-based algorithms.
This has made them unviable for topology optimization due to the high
computational cost per iteration and high dimensionality of these problems. We
propose a pre-trained neural reparameterization strategy that leads to at least
one order of magnitude decrease in iteration count when optimizing the designs
in latent space, as opposed to the conventional approach without latent
reparameterization. We demonstrate this via extensive computational experiments
in- and out-of-distribution with the training data. Although gradient-based
topology optimization is still more efficient for differentiable problems, such
as compliance optimization of structures, we believe this work will open up a
new path for problems where gradient information is not readily available (e.g.
fracture).

通过预训练的神经重参数化策略，在潜在空间中优化设计相对于传统方法而言能减少至少一个数量级的迭代次数，这在拓扑优化中具有重要意义。