Deep Electromagnetic Structure Design Under Limited Evaluation Budgets

Electromagnetic structure (EMS) design plays a critical role in developing advanced antennas and materials, but remains challenging due to high-dimensional design spaces and expensive evaluations. While existing methods commonly employ high-quality predictors or generators to alleviate evaluations, they are often data-intensive and struggle with real-world scale and budget constraints. To address this, we propose a novel method called Progressive Quadtree-based Search (PQS). Rather than exhaustively exploring the high-dimensional space, PQS converts the conventional image-like layout into a quadtree-based hierarchical representation, enabling a progressive search from global patterns to local details. Furthermore, to lessen reliance on highly accurate predictors, we introduce a consistency-driven sample selection mechanism. This mechanism quantifies the reliability of predictions, balancing exploitation and exploration when selecting candidate designs. We evaluate PQS on two real-world engineering tasks, i.e., Dual-layer Frequency Selective Surface and High-gain Antenna. Experimental results show that our method can achieve satisfactory designs under limited computational budgets, outperforming baseline methods. In particular, compared to generative approaches, it cuts evaluation costs by 75∼85%, effectively saving 20.27∼38.80 days of product designing cycle.

Electromagnetic structure (EMS) is essential to modern wireless communication, yet finding a satisfactory design often resembles searching for a needle in a haystack, where each trial is expensive and time-consuming. Existing methods are often data-intensive and struggle with real-world scale and budget constraints. To address this, we introduce Progressive Quadtree-based Search (PQS), a method that progressively explores the design space from global patterns down to local details, avoiding exhaustive trial-and-error. To curb data requirements, PQS incorporates a strategy that balances exploration and exploitation when selecting candidate designs.We evaluated PQS on two real-world industrial challenges and achieved target performance within tight computational budgets. Compared with state-of-the-art generative approaches, PQS cut evaluation costs by 75∼85%, shortening the product-design cycle by 20∼39 days.