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Record W1804043484 · doi:10.1002/rds.20051

Modeling of the perfect electromagnetic conducting boundary in the finite difference time domain method

2013· article· en· W1804043484 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

VenueRadio Science · 2013
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFinite-difference time-domain methodBoundary (topology)Boundary value problemCollocation (remote sensing)Finite difference methodMathematical analysisWedge (geometry)MathematicsComputational electromagneticsComputer sciencePoint (geometry)Electromagnetic fieldGeometryPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract The perfect electromagnetic conducting (PEMC) boundary, a nonreciprocal generalization of both perfect electric conducting (PEC) and perfect magnetic conducting (PMC) boundaries, is modeled in the finite difference time domain (FDTD) method. Since the PEMC boundary condition requires collocation of same components of both electric and magnetic fields at the boundary grids, which is not compatible with the original FDTD algorithm, its implementation in FDTD is challenging and requires modification in the algorithm. To do this task, first, the original FDTD cell is modified by inserting the required field components not present in the original cell. Then, a novel formulation is developed for updating fields' components at the boundary. Modeling of a PEMC planar interface, a corner point, and a wedge point are presented. Finally, numerical examples are presented to show stability, accuracy, and applicability of the proposed approach. Validation is achieved by comparisons with existing analytic methods and/or conventional FDTD for special cases of PEC and PMC boundaries.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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