MétaCan
Menu
Back to cohort
Record W2035556050 · doi:10.1109/lawp.2014.2348993

One-Step Leapfrog ADI-FDTD With CPML for General Orthogonal Grids

2014· article· en· W2035556050 on OpenAlexaff
Xiang-Hua Wang, Wen‐Yan Yin, Zhizhang Chen

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2014
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsDalhousie University
FundersNational Science Foundation
KeywordsFinite-difference time-domain methodPerfectly matched layerAlternating direction implicit methodComputationFinite difference methodMathematicsMathematical analysisComputer scienceApplied mathematicsAlgorithmPhysicsOptics

Abstract

fetched live from OpenAlex

The unconditionally stable one-step leapfrog alternating-direction-implicit finite-difference time-domain (ADI-FDTD) method is extended and developed for general orthogonal grids in this letter. It can be derived from either the conventional ADI-FDTD but with no mid-time computation, or simply conventional FDTD, just with one perturbation term added. The convolutional perfectly matched layer (CPML) is also derived for general orthogonal grids. The proposed developments are validated and further used for investigation of the earth-ionosphere cavity, with the Schumann resonant frequencies captured. It is numerically demonstrated that the proposed method is unconditionally stable and more efficient than the conventional FDTD method for the problems considered here.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.001
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Antennas and Wireless Propagation LettersSame topicElectromagnetic Simulation and Numerical MethodsFrench-language works237,207