How the Classical Baseline Shapes Machine-Learning Speed Claims in Topology Optimization

Abstract

Topology optimization places material in a design domain to optimize a structure's performance, such as its stiffness or heat conduction. Machine-learning methods often report large speed-ups over classical, non-learned optimization, but a slow classical code or a lower-quality learned design can exaggerate them. We re-ran released code for eight methods on one workstation and studied two in depth: Optimize Any Topology (OAT), a diffusion model that generates designs, and a graph-based neural optimizer. Replacing the classical code with an efficient implementation of the same algorithm cut these two methods' speed-ups by up to 20-fold. We then timed the classical code until its design was at least as stiff as the learned one; for OAT, we picked this point after the runs, which gives the classical code its best case. Published timings imply a 134x speed-up for OAT on a 256 x 256 grid; our matched single-design comparisons gave 1.5x to 3.5x. At matched stiffness, the graph-based optimizer was slower than the classical code in all six two-dimensional problems. Of the six other methods, two kept their advantage, one shrank, one reversed, and two were not comparable. In an exploratory audit, language models judged that 8 of 28 speed comparisons in randomly sampled, openly accessible articles lacked evidence of equal design quality; our own review counted 11, so the count depends on who judges. We provide a comparison procedure and a minimum report: classical code, hardware, design quality, what each timing includes, and data-generation and training costs.

arXiv comment32 pages, 8 figures, 10 tables. Research article: tests of released code for eight topology-optimization methods and an exploratory audit of published speed claims