ArticleScientific reports2021
Deep neural network-based automatic metasurface design with a wide frequency range.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Customizing the electric field of metalens with high degrees of freedom based on neural network.Nanophotonics (Berlin, Germany) · 2025Article
- Physical twinning for joint encoding-decoding optimization in computational optics: a review.Light, science & applications · 2025Review
- Review
- Leveraging multiplexed metasurfaces for multi-task learning with all-optical diffractive processors.Nanophotonics (Berlin, Germany) · 2024Article
- Multi-channel polarization manipulation based on graphene for encryption communication.Scientific reports · 2024Article
- Recent advances in metasurface design and quantum optics applications with machine learning, physics-informed neural networks, and topology optimization methods.Light, science & applications · 2023Review
- Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications.Biomimetics (Basel, Switzerland) · 2023Article
- A reconfigurable graphene patch antenna inverse design at terahertz frequencies.Scientific reports · 2023Article
- Rapid multi-criterial design of microwave components with robustness analysis by means of knowledge-based surrogates.Scientific reports · 2023Article
- A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning.Materials (Basel, Switzerland) · 2023Article
- Programmable VOiScience · 2022Article
- Deep learning in light-matter interactions.Nanophotonics (Berlin, Germany) · 2022Review
- On geometry parameterization for simulation-driven design closure of antenna structures.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Beyond the scope of conventional metasurface, which necessitates plenty of computational resources and time, an inverse design approach using machine learning algorithms promises an effective way for metasurface design. In this paper, benefiting from Deep Neural Network (DNN), an inverse design procedure of a metasurface in an ultra-wide working frequency band is presented in which the output unit cell structure can be directly computed by a specified design target. To reach the highest working frequency for training the DNN, we consider 8 ring-shaped patterns to generate resonant notches at a wide range of working frequencies from 4 to 45 GHz. We propose two network architectures. In one architecture, we restrict the output of the DNN, so the network can only generate the metasurface structure from the input of 8 ring-shaped patterns. This approach drastically reduces the computational time, while keeping the network's accuracy above 91%. We show that our model based on DNN can satisfactorily generate the output metasurface structure with an average accuracy of over 90% in both network architectures. Determination of the metasurface structure directly without time-consuming optimization procedures, an ultra-wide working frequency, and high average accuracy equip an inspiring platform for engineering projects without the need for complex electromagnetic theory.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.