Paper Citation Record · LEDGER

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology

As of 13 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.10418.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.10418 v1

Coverage vector

measured 29 of 29 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

29 of 29 outbound references displayed

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External citation measurements

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Outbound references

Observation 2bf09e81-7467-4372-899b-129ec287c414 · outbound

This paper cites Empowering nanophotonic applications via artificial intelligence: pathways, progress, and prospects,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Empowering nanophotonic applications via artificial intelligence: pathways, progress, and prospects,

Reference 2

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Observation 1d85d1cc-f2b6-4595-9239-160d6d10d4e6 · outbound

This paper cites Deep learning enabled inverse design in nanophotonics,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Deep learning enabled inverse design in nanophotonics,

Reference 3

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Observation 6f48db8c-50dc-4a11-9908-10becd55240e · outbound

This paper cites Quantum cascade laser,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Quantum cascade laser,

Reference 4

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Observation b0ba56f9-dd87-44b6-888d-2fd900323991 · outbound

This paper cites Quantum cascade unipolar intersubband light emitting diodes in the 8–13 μm wavelength region,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Quantum cascade unipolar intersubband light emitting diodes in the 8–13 μm wavelength region,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.379717Z

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Observation 4878f9d3-3511-415b-b370-85e7b7ce450d · outbound

This paper cites Evaluation of some scattering times for electrons in unbiased and biased single- and multiple-quantum-well structures,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Evaluation of some scattering times for electrons in unbiased and biased single- and multiple-quantum-well structures,

Reference 6

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Observation 3ec5a00d-ce2d-42fd-8fd2-e9e3dd45abee · outbound

This paper cites Femtosecond luminescence measurements of the intersubband scattering rate in AlxGa1−xAs/GaAs quantum wells under selective excitation,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Femtosecond luminescence measurements of the intersubband scattering rate in AlxGa1−xAs/GaAs quantum wells under selective excitation,

Reference 7

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Observation 95bc721b-8a43-461d-b527-1881035039e3 · outbound

This paper cites Development of a multi-objective evolutionary algorithm for strain-enhanced quantum cascade lasers,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Development of a multi-objective evolutionary algorithm for strain-enhanced quantum cascade lasers,

Reference 8

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Observation f519ca2a-9f2e-4d6e-a19d-a1dfd70db4bc · outbound

This paper cites Particle swarm optimization approach to identify optimum electrical pulse characteristics for efficient gain switching in dual wavelength quantum cascade lasers,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Particle swarm optimization approach to identify optimum electrical pulse characteristics for efficient gain switching in dual wavelength quantum cascade lasers,

Reference 9

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Observation fe863a5b-ea87-47dd-b502-b981d63ca5a7 · outbound

This paper cites QCL design engineering: automatization vs. classical approaches,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology QCL design engineering: automatization vs. classical approaches,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.321226Z

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Observation c24575ea-dea8-4bce-867e-299c07c8a424 · outbound

This paper cites Bayesian optimization of terahertz quantum cascade lasers,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Bayesian optimization of terahertz quantum cascade lasers,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.309736Z

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Observation 83461e7b-8870-4e7a-846a-6857cb41f647 · outbound

This paper cites Migrating knowledge between physical scenarios based on artificial neural networks,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Migrating knowledge between physical scenarios based on artificial neural networks,

Reference 12

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Observation b73676bb-eea4-4d2e-95e2-03a315da0600 · outbound

This paper cites Transfer-learning-assisted inverse metasurface design for 30% data savings,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Transfer-learning-assisted inverse metasurface design for 30% data savings,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.287807Z

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Observation d19c24e0-acad-4a59-bd95-cf972503bdb7 · outbound

This paper cites Effect of conduction band non-parabolicity on the optical gain of quantum cascade lasers based on the effective two- band finite difference method,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Effect of conduction band non-parabolicity on the optical gain of quantum cascade lasers based on the effective two- band finite difference method,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.276116Z

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Observation 20afa3b3-b94c-4a92-8f63-b778fd1561f1 · outbound

This paper cites Theoretical and experimental study of optical gain and linewidth enhancement factor of type-I quantum-cascade lasers,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Theoretical and experimental study of optical gain and linewidth enhancement factor of type-I quantum-cascade lasers,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.263938Z

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Observation 0e8709fd-0d4e-41e7-ac77-641a8609b422 · outbound

This paper cites Intersubband absorption linewidth in GaAs quantum wells due to scattering by interface roughness, phonons, alloy disorder, and impurities,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Intersubband absorption linewidth in GaAs quantum wells due to scattering by interface roughness, phonons, alloy disorder, and impurities,

Reference 16

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Observation 29071841-7b8e-41e6-9995-fc0b905c8c73 · outbound

This paper cites Interface-roughness-induced broadening of intersubband electroluminescence in p-SiGe and n-GaInAs/ AlInAs quantum- cascade structures,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Interface-roughness-induced broadening of intersubband electroluminescence in p-SiGe and n-GaInAs/ AlInAs quantum- cascade structures,

Reference 17

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This paper cites Intersubband linewidths in quantum cascade laser designs,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Intersubband linewidths in quantum cascade laser designs,

Reference 18

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Observation 82415ee1-3ef0-4c22-8019-dbfcfd459403 · outbound

This paper cites A Tutorial on Bayesian Optimization.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology A Tutorial on Bayesian Optimization

Reference 19

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Observation 6492ca83-71f1-4f10-bb43-087a9d779bfa · outbound

This paper cites Free-form optimization of nanophotonic devices: from classical methods to deep learning,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Free-form optimization of nanophotonic devices: from classical methods to deep learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.423542Z

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Observation 17533aee-ba21-4336-a879-7ffcce38c8ca · outbound

This paper cites Fully automatized quantum cascade laser design by genetic optimization,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Fully automatized quantum cascade laser design by genetic optimization,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.211161Z

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Observation 58b0a2c7-64c2-4bf1-9d56-f0b2d67a6d26 · outbound

This paper cites Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Géron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.199027Z

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Observation 9ef1030f-3d58-4af6-8a93-cc56faf5f791 · outbound

This paper cites Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and fu- ture prospects,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and fu- ture prospects,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.187698Z

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Observation 05f25391-ee4a-4432-bcf9-607a1059a8cf · outbound

This paper cites Large-scale photonic inverse design: computational challenges and break- throughs,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Large-scale photonic inverse design: computational challenges and break- throughs,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.173725Z

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Observation aed83c03-3edf-4228-b02d-030569f5399b · outbound

This paper cites Multi-objective optimization using genetic algorithms: A tutorial,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Multi-objective optimization using genetic algorithms: A tutorial,

Reference 25

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Observation 073fdfdf-a244-4caa-9646-3ca6ec258bf3 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Optuna: A next-generation hyperparameter optimization framework,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.147053Z

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Observation f4edda4d-2533-4c18-a277-1015c43c62f9 · outbound

This paper cites Physics-enhanced deep surrogates for partial differential equations,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Physics-enhanced deep surrogates for partial differential equations,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.133671Z

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Observation 6b8f2264-4d18-4884-b923-a5fd61e8e096 · outbound

This paper cites Lee, Python machine learning, New Jersey, John Wiley & Sons, 2019.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Lee, Python machine learning, New Jersey, John Wiley & Sons, 2019

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:32.058476Z

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Observation 86d0ddc3-d752-4f6c-8a82-704361de610b · outbound

This paper cites Harrison and A.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Harrison and A

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:31.902246Z

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Observation a4f675a9-608d-44d4-945a-f1cf853b797f · outbound

This paper cites Simulating 500 million years of evolution with a language model,.

Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning (PBTL) methodology Simulating 500 million years of evolution with a language model,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:32:31.695202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

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