Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency | Paper Citation Record

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This paper cites Inverse design in nanophotonics ,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Inverse design in nanophotonics ,

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Silicon photonics circuit design: methods, tools and challenges,

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This paper cites Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale ,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale ,

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Superior thermal conductivity of single -layer graphene,

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Response of graphene to femtosecond high -intensity laser irradiation,

Reference 5

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Coherent nonlinear optical response of graphene,

Reference 6

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This paper cites Graphene photonics, plasmonics, and broadband optoelectronic devices,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Graphene photonics, plasmonics, and broadband optoelectronic devices,

Reference 7

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This paper cites Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Tunable plasmon induced transparency in a metallodielectric grating coupled with graphene metamaterials,

Reference 8

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This paper cites Fine structure constant defines visual transparency of graphene,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Fine structure constant defines visual transparency of graphene,

Reference 9

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency The rise of graphene,

Reference 10

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency A graphene-based broadband optical modulator,

Reference 11

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Photothermoelectric and photoelectric contributions to light detection in metal –graphene–metal photodetectors,

Reference 12

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Graphene-based transparent strain sensor,

Reference 13

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency An ultra -broadband multilayered graphene absorber,

Reference 14

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This paper cites Dynamically tunable plasmon induced transparency in a graphene -based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Dynamically tunable plasmon induced transparency in a graphene -based nanoribbon waveguide coupled with graphene rectangular resonators structure on sapphire substrate,

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Ultra -compact polarization beam splitter utilizing a graphene -based asymmetrical directional coupler,

Reference 16

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Chemically modulated graphene diodes,

Reference 17

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Plasmonically induced transparency in double -layered graphene nanoribbons,

Reference 18

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Investigation of multiband plasmonic metamaterial perfect absorber s based on graphene ribbons by the phase-coupled method,

Reference 19

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,

Reference 20

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Deep learning in neural networks: An overview,

Reference 21

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Recent trends in deep learning based natural lang uage processing,

Reference 22

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Reference 23

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency A review of unsupervised feature learning and deep learning for time -series modeling,

Reference 24

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Reference 25

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Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Playing Atari with Deep Reinforcement Learning

Reference 26

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Reference 27

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Reference 28

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Reference 29

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Reference 30

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Reference 31

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Reference 32

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Reference 33

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Reference 34

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Reference 35

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Reference 36

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Reference 37

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Reference 38

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Reference 39

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Reference 40

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Reference 41

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Reference 42

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Reference 43

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Reference 44

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Reference 45

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Reference 46

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Reference 47

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Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.506643Z

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Reference 49

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Observation c5006c2e-9744-470a-8b5d-1e66eb44b4bc · outbound

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Reference 50

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This paper cites The inverse design of structural color using machine learning,.

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Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.472180Z

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Observation cbb74b67-cbf2-4eee-95e3-737cc9224cd2 · outbound

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Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.452502Z

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This paper cites Finding the op tical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks,.

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Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.429147Z

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Observation 647cb549-03ab-4699-abe2-04c4c860efe6 · outbound

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Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.411820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b4f84474-7632-4cc1-b867-0487088144fb · outbound

This paper cites Ultra-compact photonic structure design for strong light confinement and coupling into nano-waveguide,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Ultra-compact photonic structure design for strong light confinement and coupling into nano-waveguide,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.387264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 774c2761-10b1-4a3a-96d0-bbfa5f402e9e · outbound

This paper cites Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photon ic coupler devices,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photon ic coupler devices,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.368820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e6a48255-aa7e-4b98-ac7f-1f1ac0524ecb · outbound

This paper cites Photonics inverse design: pairing deep neural networks with evolutionary algorithms,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Photonics inverse design: pairing deep neural networks with evolutionary algorithms,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.205280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 58b50d52-9dc0-4863-85d5-cda052838461 · outbound

This paper cites Ultranarrow-band wavelength -selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Ultranarrow-band wavelength -selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.185846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 171340ea-6223-42c2-92e6-ca53a6f96d9c · outbound

This paper cites Mapping the global design space of nanophotonic components using machine learning pattern recognition,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Mapping the global design space of nanophotonic components using machine learning pattern recognition,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.169351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cf26be39-dae4-4eaf-9d93-59911f77cf3f · outbound

This paper cites an unresolved cited work.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:21:38.152268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 53c0a0e4-9ef7-47c3-b4f7-f98bc9915f74 · outbound

This paper cites A review on applications of ANN and SVM for building electrical energy consumption forecasting,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency A review on applications of ANN and SVM for building electrical energy consumption forecasting,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.138838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 572edcb6-1050-4a1c-85ae-525dc110613b · outbound

This paper cites Cavity-enhanced seco nd-harmonic generation via nonlinear -overlap optimization,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Cavity-enhanced seco nd-harmonic generation via nonlinear -overlap optimization,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.122814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1b3a763d-35c1-4115-81b9-7f1d00fdaf53 · outbound

This paper cites Adjoint method and inverse design for nonlinear nanophotonic devices,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Adjoint method and inverse design for nonlinear nanophotonic devices,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.102234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation abb1b9f9-6c2c-47bd-a763-bb23351403f8 · outbound

This paper cites Training of photonic neural networks through in situ backpropagation and gradient measurement,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Training of photonic neural networks through in situ backpropagation and gradient measurement,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-14T15:21:37.130582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1be1c086-d22c-48b1-be7f-0c62211a2ac8 · outbound

This paper cites Inverse design and demonstration of a compact and b roadband on -chip wavelength demultiplexer,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Inverse design and demonstration of a compact and b roadband on -chip wavelength demultiplexer,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.065853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1d17aecb-326d-43fb-91dc-265b0044406f · outbound

This paper cites Inverse -designed metastructures that solve equations,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Inverse -designed metastructures that solve equations,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.041279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 077f7358-52ef-441b-9756-a382cd54be24 · outbound

This paper cites An integra ted- nanophotonics polarization beamsplitter with 2.4 × 2.4 μm 2 footprint,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency An integra ted- nanophotonics polarization beamsplitter with 2.4 × 2.4 μm 2 footprint,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.019396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f2a7e10c-e7ab-48c7-b7f3-bc10a3b7f799 · outbound

This paper cites Genetic -algorithm-optimized wideband on - chip polarization rotator with an ultrasmall footprint,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Genetic -algorithm-optimized wideband on - chip polarization rotator with an ultrasmall footprint,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:38.000011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8a336570-4e9f-45f5-88f1-bb1d77fd86db · outbound

This paper cites Binary particle swarm optimized 2× 2 power splitters in a standard foundry silicon photonic platform,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Binary particle swarm optimized 2× 2 power splitters in a standard foundry silicon photonic platform,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.981611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e1174ec9-d3fe-46fd-8945-fb55c3135503 · outbound

This paper cites Efficient training and design of photonic neural network through neuroevolution,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Efficient training and design of photonic neural network through neuroevolution,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.962214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e4fb2c70-ed23-4b7f-8567-79b419a9ba4f · outbound

This paper cites Complex Inverse Design of Meta -optics by Segmented Hierarchical Evolutionary Algori thm,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Complex Inverse Design of Meta -optics by Segmented Hierarchical Evolutionary Algori thm,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.945619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8f66c081-d28d-478a-b392-9539e06d85f7 · outbound

This paper cites Stochastic collocation for device -level variability analysis in integrated photonics,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Stochastic collocation for device -level variability analysis in integrated photonics,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.926227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 32c9d305-8dc1-4968-9933-57110cbfe555 · outbound

This paper cites Nano -optics of surface plasmon polaritons,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Nano -optics of surface plasmon polaritons,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.911861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f93757fe-654c-4643-ad3c-ba2929325bf4 · outbound

This paper cites Plasmonics in graphene at infrared frequencies,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Plasmonics in graphene at infrared frequencies,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.897881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b6d5277e-4b7c-4351-b7b0-1ce8becf9993 · outbound

This paper cites Graphene -based tunable broadband hyperlens for far -field subdiffraction imaging at mid -infrared frequencies,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Graphene -based tunable broadband hyperlens for far -field subdiffraction imaging at mid -infrared frequencies,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.882505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d396f790-c7f1-4e91-9105-b6b49a441bed · outbound

This paper cites Transformation optics using graphene,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Transformation optics using graphene,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.865539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ec1f1e38-c27c-4d43-a81b-10fb330b96af · outbound

This paper cites Graphene plasmonics for tunable t erahertz metamaterials,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Graphene plasmonics for tunable t erahertz metamaterials,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.850176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 09ed9b4f-5371-4178-8f93-50e6757f872c · outbound

This paper cites Graphene -based tunable hyperbolic metamaterials and enhanced near -field absorption,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Graphene -based tunable hyperbolic metamaterials and enhanced near -field absorption,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.833355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 62b72d20-a231-4b0a-8eb6-374bac568003 · outbound

This paper cites Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal - graphene metamaterials,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Active modulation of electromagnetically induced transparency analogue in terahertz hybrid metal - graphene metamaterials,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.815226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d9b19d6d-ef46-48b1-bd1b-737103be34fd · outbound

This paper cites A perfect absorber made of a graphene micro -ribbon metamaterial,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency A perfect absorber made of a graphene micro -ribbon metamaterial,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.795980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 37ea94dd-e4ad-4363-9c38-7c7f245d88ae · outbound

This paper cites Mid -infrared plasmonic biosensing with graphene,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Mid -infrared plasmonic biosensing with graphene,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.775970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0bca6739-eea4-4183-8024-cf6ee3ab1ca6 · outbound

This paper cites Tunable broadband plasmonic field enhancement on a graphene surface using a normal -incidence plane wave at mid-infrared frequencies,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Tunable broadband plasmonic field enhancement on a graphene surface using a normal -incidence plane wave at mid-infrared frequencies,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.747918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 881ab25e-2cfd-40f1-b6d8-e332ba234bd9 · outbound

This paper cites Investigation of the graphene based planar plasmonic filters,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Investigation of the graphene based planar plasmonic filters,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.729710Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 81e0964e-ea4e-4ed9-9ace-e920e7538b9c · outbound

This paper cites Edge and waveguide terahertz surface plasmon modes in graphene microribb ons,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Edge and waveguide terahertz surface plasmon modes in graphene microribb ons,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.711507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7306df8b-8fab-403b-8e5d-f5ad5848e857 · outbound

This paper cites Plasmon induced absorption in a graphene -based nanoribbon waveguide system and its applications in logic gate and sensor,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Plasmon induced absorption in a graphene -based nanoribbon waveguide system and its applications in logic gate and sensor,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.689899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 72bb2983-8d0d-43ec-97a2-6b2d0e7917ce · outbound

This paper cites Phase - coupled plasmon-induced transparency,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Phase - coupled plasmon-induced transparency,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.672073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation caabf22e-519d-4df4-a460-7770254f594c · outbound

This paper cites Inverse design and demonstration of a compact and broadband on - chip wavelength demultiplexer,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Inverse design and demonstration of a compact and broadband on - chip wavelength demultiplexer,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.656725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 32a52bf9-dfd8-4252-8a5d-897d4a209ffb · outbound

This paper cites Topology optimized mode multiplexing in silicon -on-insulator photonic wire waveguides,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Topology optimized mode multiplexing in silicon -on-insulator photonic wire waveguides,

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.642136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3b14126e-4927-47b4-b1b4-0bc108e60d6f · outbound

This paper cites Edge -reflection phase directed plasmonic resonances on graphene nano-structures,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Edge -reflection phase directed plasmonic resonances on graphene nano-structures,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.624309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation ea41fde7-997b-493d-8eaf-195ca73f928d · outbound

This paper cites High -contrast electro -optic modulation of spatial light induced by graphene -integrated Fabry -Pé rot microcavity,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency High -contrast electro -optic modulation of spatial light induced by graphene -integrated Fabry -Pé rot microcavity,

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.609853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9c24a201-7903-449e-a4af-d62811b10c45 · outbound

This paper cites Methods based on k -nearest neighbor regression in the prediction of basal area diameter distribution,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Methods based on k -nearest neighbor regression in the prediction of basal area diameter distribution,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.588849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b9fcd8e2-3da7-4d70-8d8c-fb3f7a1fb822 · outbound

This paper cites A novel decision tree regression -based fault distance estimation scheme for transmission lines,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency A novel decision tree regression -based fault distance estimation scheme for transmission lines,

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.567938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c068d2d5-ecbf-41e5-89b3-eb696da0b1fc · outbound

This paper cites Classification and regression by randomForest,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Classification and regression by randomForest,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.548920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6d2653b9-9380-4036-ac71-d84d228b4994 · outbound

This paper cites Extr emely randomized trees,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Extr emely randomized trees,

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.534569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5205f9b0-7fe2-4193-bc23-68e595003e5a · outbound

This paper cites an unresolved cited work.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Unresolved cited work

Reference 95

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:21:37.516896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6cdfbc97-0587-430b-bfed-798e23008cd0 · outbound

This paper cites Scikit -learn: Machine learning in Python,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency Scikit -learn: Machine learning in Python,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.501998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d2d198b3-0714-4242-ba47-449e70fbcedc · outbound

This paper cites The MATLAB genetic algorithm toolbox,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency The MATLAB genetic algorithm toolbox,

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.483005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a32ac774-c25c-4341-8366-8790ddcc97d3 · outbound

This paper cites G enetic quantum algorithm and its application to combinatorial optimization problem,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency G enetic quantum algorithm and its application to combinatorial optimization problem,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.465220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d6e3c156-7766-4799-8cfd-7f75730777a2 · outbound

This paper cites A fast and elitist multiobjective genetic algorithm: NSGA -II,.

Machine learning and evolutionary algorithm studies of graphene metamaterials for optimized plasmon-induced transparency A fast and elitist multiobjective genetic algorithm: NSGA -II,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:21:37.442149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers