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Double-deep Q-learning to increase the efficiency of metasurface holograms
We use a double deep Q-learning network (DDQN) to find the right material type and the optimal geometrical design for metasurface holograms to reach high efficiency. The DDQN acts like an intelligent sweep and could identify the optimal results in ~5.7 billion states after only 2169 steps. The optim...
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| Publicado no: | Sci Rep |
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| Main Authors: | , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Nature Publishing Group UK
2019
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6662763/ https://ncbi.nlm.nih.gov/pubmed/31358783 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41598-019-47154-z |
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