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Generalized Tensor FDTD Method for Sloped Plasmonic Interfaces

2019· article· en· W2982637461 on OpenAlexaff
Qiming Zhao, Costas D. Sarris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFinite-difference time-domain methodPlasmonNanorodPermittivityConvergence (economics)Tensor (intrinsic definition)Finite difference methodMaxwell's equationsElectromagnetic field solverComputer scienceMaterials scienceMathematical analysisMathematicsPhysicsOpticsElectric fieldGeometryOptoelectronicsDielectricNanotechnology

Abstract

fetched live from OpenAlex

A tensor finite-difference time-domain (FDTD) method for sloped interfaces is generalized to dispersive media and applied to the study of plasmonic periodic structures formed by silver nanorods. Conventional staircased FDTD exhibits poor convergence properties in this situation, as plasmonic fields are strongly localized right where staircasing errors occur, namely at the air-silver interface. Alternative methods that have been proposed for this problem include the use of a triangular mesh or effective permittivity models that lead to a fourth-order auxiliary differential equation (ADE) connecting D and E at the interface. The proposed approach offers high accuracy, still employing a rectangular FDTD mesh, thus striking a very appealing balance between accuracy and computational efficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.720
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.299
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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