2019/09/10 by Stephan Hoyer, Hoyer, Stephan, Jascha Sohl‐Dickstein +3 · 3 citations
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Evolutionary Computing (cs.NE) #Structural Health Monitoring Techniques #Topology Optimization in Engineering
paper · pdf · doi:10.48550/arxiv.1909.04240
openalex publication_date 2019/09/10 · openalex created_date 2019/09/19 · openalex updated_date 2026/07/28
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 produces the best design 50% more often than the best baseline method.