ArticleNanoscale advances2022
Optimization of metamaterials and metamaterial-microcavity based on deep neural networks.
Article in Nanoscale advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Review of Machine Learning Applications in Mechanical Metamaterial Design.Materials (Basel, Switzerland) · 2026Review
- Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
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Authors and funding
3 authors.
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Abstract
Computational inverse-design and forward prediction approaches provide promising pathways for on-demand nanophotonics. Here, we use a deep-learning method to optimize the design of split-ring metamaterials and metamaterial-microcavities. Once the deep neural network is trained, it can predict the optical response of the split-ring metamaterial in a second which is much faster than conventional simulation methods. The pretrained neural network can also be used for the inverse design of split-ring metamaterials and metamaterial-microcavities. We use this method for the design of the metamaterial-microcavity with the absorptance peak at 1310 nm. Experimental results verified that the deep-learning method is a fast, robust, and accurate method for designing metamaterials with complex nanostructures.
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Registered trials
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