MétaCan
Menu
Back to cohort
Record W2101746877 · doi:10.1109/aps.1989.134609

A simple absorbing boundary algorithm for the FDTD method with arbitrary incidence angle

2003· article· en· W2101746877 on OpenAlexaff
J.E. Roy, Dennis H. Choi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsFinite-difference time-domain methodIncidence (geometry)Boundary (topology)AlgorithmReflection (computer programming)Simple (philosophy)Boundary value problemMathematicsAngle of incidence (optics)Space (punctuation)Mathematical analysisComputer scienceOpticsPhysicsGeometry

Abstract

fetched live from OpenAlex

An algorithm was developed by X. Zhang and K.K. Mei (1988) for simulating the propagation of the quasi-TEM wave in a microstrip structure with the wave impinging at normal incidence onto the absorbing boundary. The amount of reflection was reported to be on the order of 3 to 5%. For the present work, the authors rationalize the concept of the absorbing boundary for the finite-difference time-domain (FDTD) method in terms of phase and group velocities, improve on the algorithm with a resulting decrease of two orders of magnitude in the amount of reflection at the tuning frequency, and generalize the algorithm to encompass the case of arbitrary incidence angle. Yet, the algorithm remains very simple and very local, requiring the knowledge of the field values at points located only one space increment away from the absorbing boundary.>

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.006

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 head0.287
Teacher spread0.273 · 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

Citations1
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicElectromagnetic Simulation and Numerical MethodsFrench-language works237,207