U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts

arXiv:2606.04658v1 Announce Type: cross Abstract: Optimizing urban layouts for climate adaptation requires balancing building density with cold-air ventilation. Because physics-based climate simulations are computationally expensive, planners typically evaluate fewer than ten manual designs. gls{qd} algorithms offer a way to systematically illuminate the design space, but they require surrogate models to be practical. In this paper, we replace a slow, regulatory physics simulator with a spatial deep-learning surrogate (U-Net) inside an offline MAP-Elites loop. We systematically compare this spatial approach with a traditional gls{gp} surrogate across different training-data strategies (quasi-random Sobol sampling vs. active gls{qd} bootstrapping). Our results reveal that scalar gls{gp} surrogates fail catastrophically when trained on random samples, requiring expensive, actively generated gls{qd} archives to generalize. In contrast, the spatial inductive bias of the U-Net allows it to learn the underlying physics mapping robustly (R^2 = 0.996), completely independent of the training data source. This allows offline gls{qd} optimization to achieve highly accurate fitness rankings (rho = 0.994) using only a one-time batch of random training samples. The resulting pipeline, deployed in the open-source OpenSKIZZE tool, generates thousands of diverse, climate-evaluated building layouts in under ten minutes.

Source: arXiv cs.LG | 2026-06-04