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Record W2154537983 · doi:10.1109/aps.1997.630163

Computationally efficient algorithms for multi-term dielectric dispersion in FDTD

2002· article· en· W2154537983 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFinite-difference time-domain methodDebyeLorentz transformationConvolution (computer science)Nonlinear systemDispersion (optics)Differential equationAlgorithmMathematicsLinear differential equationApplied mathematicsComputer scienceMathematical analysisPhysicsOpticsClassical mechanics

Abstract

fetched live from OpenAlex

Several techniques have been described for modeling dispersive phenomena in media described by multi-pole Debye or Lorentz models. The recursive convolution approach is difficult to derive, requires complex arithmetic and assumes that the medium is linear. Another category of methods utilizes auxiliary differential equations. Since the medium does not have to be linear this method is particularly attractive for modeling nonlinear effects. In this paper the auxiliary differential equation method is reformulated so that the solution of the system of linear equations is no longer necessary. Three second order algorithms for Debye and Lorentz dispersion with are obtained. These algorithms require fewer or equal number of unknowns than the corresponding convolution schemes, but are not limited to linear media.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.668
Threshold uncertainty score0.309

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)0.0000.000

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 head0.046
GPT teacher head0.302
Teacher spread0.257 · 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