Gradient-free neural topology optimization

arXiv:2403.04937v1 Announce Type: new
Abstract: 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 …

abstract algorithms arxiv computational converge cost cs.lg cs.na dimensionality free function gradient iteration math.na optimization per them topology type