https://doi.org/10.1051/epjam/2025006
Review
AI-driven approaches in electromagnetic metamaterials design and application: a review
1
School of Materials Science and Engineering, Shandong University, Jinan 250061, PR China
2
State Key Laboratory of Coatings for Advanced Equipment, Jinan 250061, PR China
3
Shandong Institute of Non-metallic Materials, Jinan 250031, PR China
4
Shandong Key Laboratory of Metamaterial and Electromagnetic Manipulation Technology, Shandong University,
Jinan 250061, PR China
* e-mail: 13656407707@139.com
** e-mail: zhangzidong@sdu.edu.cn
Received:
23
October
2025
Accepted:
9
November
2025
Published online: 22 December 2025
At present, the increasingly sophisticated functional demands for electromagnetic metamaterials pose significant challenges to conventional design approaches reliant on numerical simulation and empirical knowledge, particularly in terms of computational efficiency and design capability. The integration of artificial intelligence has revitalized metamaterials research, enabling rapid forward performance prediction from structural parameters, inverse design from target properties to structural configurations, as well as adaptive functional enhancement and physical mechanism interpretation. This review examines two dominant pathways in AI-assisted electromagnetic metamaterial design and application: forward prediction coupled with inverse design, and adaptive control integrated with physical insight. We summarize recent advances in deep learning and generative models that enhance design efficiency, generalization capability, and interpretability of AI-driven metamaterial models. Furthermore, we explore emerging trends and persistent challenges in developing intelligent, self-adaptive, and multiphysics-coupled metamaterial systems powered by artificial intelligence.
Key words: Electromagnetic metamaterials / artificial intelligence-driven / forward prediction and inverse design / mechanism analysis / intelligent design
© H. Yu et al., Published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
