Neural reparameterization improves structural optimization

Stephan Hoyer, Jascha Sohl-Dickstein, Sam Greydanus

Keywords: deep image prior, untrained neural networks, structural optimization

Abstract: Structural optimization is a popular method for designing objects such as bridge trusses, airplane wings, and optical devices. Unfortunately, the quality of solutions depends heavily on how the problem is parameterized. In this paper, we propose using the implicit bias over functions induced by neural networks to improve the parameterization of structural optimization. Rather than directly optimizing densities on a grid, we instead optimize the parameters of a neural network which outputs those densities. This reparameterization leads to different and often better solutions. On a selection of 116 structural optimization tasks, our approach produced significantly better designs than baseline methods.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/neural-reparameterization-improves-structural/code)