paper-with-me

홈 › Papers

Deep Electromagnetic Structure Design Under Limited Evaluation Budgets

2025-06-24 · Shijian Zheng, Fangxiao Jin, Shuhai Zhang, Quan Xue, Mingkui Tan

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.

📄 PDF Abstract BibTeX arXiv:2506.19384

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations

2023-10-29 · NeurIPS 2023 11 · Jungtaek Kim, Mingxuan Li, Oliver Hinder, Paul W. Leu

Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting diodes. To design and understand these nano…

Benchmarking Data-driven Surrogate Simulators for Artificial Electromagnetic Materials

2021-11-06 · NeurIPS 2021 11 · Yang Deng*, Juncheng Dong*, Simiao Ren*, Omar Khatib 외

Artificial electromagnetic materials (AEMs), including metamaterials, derive their electromagnetic properties from geometry rather than chemistry. With the appropriate geometric design, AEMs have achieved exotic properti…

BenchmarkingNeural Network simulation

Anchor-Controlled Generative Adversarial Network for High-Fidelity Electromagnetic and Structurally Diverse Metasurface Design

2024-08-29 · Yunhui Zeng, Hongkun Cao, Xin Jin

Metasurfaces, capable of manipulating light at subwavelength scales, hold great potential for advancing optoelectronic applications. Generative models, particularly Generative Adversarial Networks (GANs), offer a promisi…

DiversityGenerative Adversarial Network

A Novel Modeling Approach for All-Dielectric Metasurfaces Using Deep Neural Networks

2019-06-08 · Sensong An, Clayton Fowler, Bowen Zheng, Mikhail Y. Shalaginov 외

Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target elect…

All

Using Loaded N-port Structures to Achieve the Continuous-Space Electromagnetic Channel Capacity Bound

2022-05-25 · Zixiang Han, Shanpu Shen, Yujie Zhang, Shiwen Tang 외

A method for achieving the continuous-space electromagnetic channel capacity bound using loaded N-port structures is described. It is relevant for the design of compact multiple-input multiple-output (MIMO) antennas that…