How the Classical Baseline Shapes Machine-Learning Speed Claims in Topology Optimization
Topology optimization places material so that a structure resists deformation under given loads. Machine-learning methods often report large speed-ups over classical, non-learned optimization, but a slow classical code or a less stiff 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. Speeding up only the classical code's finite-element solver 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 read this point o...
| Authors | Shaoliang Yang, Jun Wang |
|---|---|
| Primary category | cs.CE |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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