ArticleNanophotonics (Berlin, Germany)2025
High accuracy inverse design of reconfigurable metasurfaces with transmission-reflection-integrated achromatic functionalities.
Article in Nanophotonics (Berlin, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Deep Learning Inverse Design of Phase-Change Reconfigurable Terahertz Metadevices for Multidimensional Secure Communication.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Large-Aperture Polarization-Independent Broadband Achromatic All-Dielectric Metalens for Terahertz Focusing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence algorithms based on deep neural network (DNN) have become an effective tool for conceiving metasurfaces recently. However, the complex and sharp resonances of metasurfaces will tremendously increase the training difficulty of DNNs with non-negligible prediction errors, which hinders their development in designing multifunctional metasurfaces. To overcome the obstacles, the interaction mechanisms between meta-atoms and terahertz (THz) waves via multipole decomposition are investigated to establish a high-quality dataset, which can decrease the complexity of DNN and improve the prediction accuracy. Meanwhile, transfer learning is also employed to reduce the large quantity of training data required by the DNN. Accordingly, two broadband and transmission-reflection-integrated reconfigurable metasurfaces for focused vortex beam generation are inversely designed by counter propagating the DNN with fraction error less than 10
Indexed as
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.