Authors
- Hao Li Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, Shandong, China
- Andrey Bogdanov Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, Shandong, China School of Physics and Engineering, ITMO University, St. Petersburg 197101, Russia
DOI:
https://doi.org/10.1609/aaai.v40i1.37025Abstract
Metasurfaces are ultrathin, engineered materials composed of nanostructures that manipulate light in ways unattainable by natural materials. Recent advances have leveraged computational optimization, machine learning, and deep learning to automate their design. However, existing approaches exhibit two fundamental limitations: (1) they often restrict the model to generating only a subset of design parameters, and (2) they rely on heavily downsampled spectral targets, which compromises both the novelty and accuracy of the resulting structures. The core challenge lies in developing a generative model capable of exploring a large, unconstrained design space while precisely capturing the intricate physical relationships between material parameters and their high-resolution spectral responses. In this paper, we introduce MetaDiT, a novel framework for high-fidelity metasurface design that addresses these limitations. Our approach leverages a robust spectrum encoder pretrained with contrastive learning, providing strong conditional guidance to a Diffusion Transformer-based backbone. Experiments demonstrate that MetaDiT outperforms existing baselines in spectral accuracy, we further validate our method through extensive ablation studies.
How to Cite
Li, H., & Bogdanov, A. (2026). MetaDiT: Enabling Fine-grained Constraints in High-degree-of Freedom Metasurface Design. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 606–614. https://doi.org/10.1609/aaai.v40i1.37025
Issue
Section
AAAI Technical Track on Application Domains I