MétaCan
Menu
Back to cohort
Record W2143114517 · doi:10.1109/mwsym.2012.6259497

A subgridding scheme using hybrid one-step leapfrog ADI-FDTD and FDTD methods

2012· article· en· W2143114517 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 institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsFinite-difference time-domain methodInterpolation (computer graphics)Stability (learning theory)Finite difference methodPolygon meshNumerical stabilityComputer scienceFinite differenceAlgorithmMathematicsApplied mathematicsComputational scienceNumerical analysisMathematical analysisGeometryOpticsPhysicsImage (mathematics)

Abstract

fetched live from OpenAlex

A novel subgridding scheme that hybridizes the recently developed unconditionally stable one-step leapfrog alternately-direction-implicit finite-difference time-domain (ADI-FDTD) method and the conventional finite-difference time-domain (FDTD) method is proposed. The conventional explicit FDTD method is applied to coarse mesh regions while the leapfrog ADI-FDTD method to locally subgridded mesh regions. The difference between the proposed subgridding scheme and the existing ones lie in the fact that only spatial interpolation of fields is required at the interface between coarse and subgridded meshes. As a result, computational efficiency is improved while numerical stability maintained. Both stability and efficiency are verified through numerical experiments in simulating a substrate integrated waveguide.

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)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.899
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.056
GPT teacher head0.351
Teacher spread0.294 · 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