Hyperparameter-free minimum-lengthscale constraints for topology optimization

Abstract

The geometric constraints of Zhou et al. (2015) are a widely used technique in topology/freeform optimization to impose minimum lengthscales for manufacturability. However, its efficiency degrades as design binarization is increased, and it requires heuristic tuning of multiple hyperparameters. In this work, we derive analytical hyperparameters from first principles, depending only on the target lengthscale. We present results for both conic and PDE-based filtering schemes, showing that the latter is less robust due to the singularity of its underlying Green’s function. To address this, we also introduce a double-filtering approach to obtain a well-behaved PDE-based filter. Combined with our derived hyperparameters, we obtain a straightforward strategy for enforcing lengthscales using geometric constraints, with minimal hyperparameter tuning. A key enabling factor is the recent subpixel-smoothed projection (SSP) method (Hammond et al. 2025), which facilitates the rapidly converging optimization of almost-everywhere binary designs. The effectiveness of our method is demonstrated for several photonics and heat-transfer inverse-design problems.