On-Demand Inverse Design of Metamaterials Using Deep Neural Networks with Bayesian Optimization
- authored by
- Than V. Tran, S. S. Nanthakumar, Timon Rabczuk, Xiaoying Zhuang
- Abstract
Recent advances in metamaterial design highlight methodologies tailoring metamaterials to achieve target behaviors in specific applications. This investigation presents a data-driven design approach using deep neural networks for the on-demand inverse design of metamaterials to address limitations inherent in traditional methods. We propose an efficient Bayesian optimization framework for deep neural network hyperparameter optimization. The desired properties being the target bandgap width and bandgap midfrequency, the design model proposes candidate unit cell topologies. The methodology demonstrates exceptional accuracy in both the forward prediction and inverse design of 2-dimensional metamaterial structures, with particular emphasis on bandgap characteristics. Statistical analysis reveals R2 coefficients exceeding 0.99, validating the model’s predictive capabilities. The demonstrated framework represents a substantial advancement in computational metamaterial design, offering potential applications across multiple materials science and engineering domains.
- Organisation(s)
-
Institute of Photonics
- External Organisation(s)
-
Bauhaus-Universität Weimar
Tongji University
- Type
- Article
- Journal
- Intelligent Computing
- Volume
- 4
- No. of pages
- 10
- Publication date
- 19.08.2025
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Human-Computer Interaction, Computer Vision and Pattern Recognition, Computer Science Applications, Electrical and Electronic Engineering, Artificial Intelligence
- Electronic version(s)
-
https://doi.org/10.34133/icomputing.0139 (Access:
Open)
-
Details in the research portal "Research@Leibniz University"
