These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
136 related articles for article (PubMed ID: 31825227)
21. Deep Neural Network Inverse Design of Integrated Photonic Power Splitters. Tahersima MH; Kojima K; Koike-Akino T; Jha D; Wang B; Lin C; Parsons K Sci Rep; 2019 Feb; 9(1):1368. PubMed ID: 30718661 [TBL] [Abstract][Full Text] [Related]
22. Optimization of metamaterials and metamaterial-microcavity based on deep neural networks. Lan G; Wang Y; Ou JY Nanoscale Adv; 2022 Nov; 4(23):5137-5143. PubMed ID: 36504733 [TBL] [Abstract][Full Text] [Related]
24. Exploring the Magnetic and Electric Side of Light through Plasmonic Nanocavities. Ernandes C; Lin HJ; Mortier M; Gredin P; Mivelle M; Aigouy L Nano Lett; 2018 Aug; 18(8):5098-5103. PubMed ID: 30001486 [TBL] [Abstract][Full Text] [Related]
25. Plasmonic Metamaterials for Nanochemistry and Sensing. Wang P; Nasir ME; Krasavin AV; Dickson W; Jiang Y; Zayats AV Acc Chem Res; 2019 Nov; 52(11):3018-3028. PubMed ID: 31680511 [TBL] [Abstract][Full Text] [Related]
27. Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network. Li R; Gu X; Shen Y; Li K; Li Z; Zhang Z Nanomaterials (Basel); 2022 Apr; 12(8):. PubMed ID: 35458079 [TBL] [Abstract][Full Text] [Related]
29. Comparison of Different Neural Network Architectures for Plasmonic Inverse Design. Wu Q; Li X; Wang W; Dong Q; Xiao Y; Cao X; Wang L; Gao L ACS Omega; 2021 Sep; 6(36):23076-23082. PubMed ID: 34549108 [TBL] [Abstract][Full Text] [Related]
30. Finding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks. Sajedian I; Kim J; Rho J Microsyst Nanoeng; 2019; 5():27. PubMed ID: 31240107 [TBL] [Abstract][Full Text] [Related]
31. Experimental demonstration of linear and spinning Janus dipoles for polarisation- and wavelength-selective near-field coupling. Picardi MF; Neugebauer M; Eismann JS; Leuchs G; Banzer P; Rodríguez-Fortuño FJ; Zayats AV Light Sci Appl; 2019; 8():52. PubMed ID: 31231518 [TBL] [Abstract][Full Text] [Related]
32. Multipolar interference effects in nanophotonics. Liu W; Kivshar YS Philos Trans A Math Phys Eng Sci; 2017 Mar; 375(2090):. PubMed ID: 28220008 [TBL] [Abstract][Full Text] [Related]
33. Artificial Neural Network-Based Prediction of the Optical Properties of Spherical Core-Shell Plasmonic Metastructures. Vahidzadeh E; Shankar K Nanomaterials (Basel); 2021 Mar; 11(3):. PubMed ID: 33806266 [TBL] [Abstract][Full Text] [Related]
34. Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials. Ma W; Cheng F; Liu Y ACS Nano; 2018 Jun; 12(6):6326-6334. PubMed ID: 29856595 [TBL] [Abstract][Full Text] [Related]
35. Plasmonics for extreme light concentration and manipulation. Schuller JA; Barnard ES; Cai W; Jun YC; White JS; Brongersma ML Nat Mater; 2010 Mar; 9(3):193-204. PubMed ID: 20168343 [TBL] [Abstract][Full Text] [Related]
36. Double-resonant enhancement of third-harmonic generation in graphene nanostructures. You JW; You J; Weismann M; Panoiu NC Philos Trans A Math Phys Eng Sci; 2017 Mar; 375(2090):. PubMed ID: 28220005 [TBL] [Abstract][Full Text] [Related]
37. MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling. Mutasa S; Chang PD; Ruzal-Shapiro C; Ayyala R J Digit Imaging; 2018 Aug; 31(4):513-519. PubMed ID: 29404850 [TBL] [Abstract][Full Text] [Related]