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Record W4244662841 · doi:10.1002/0471654507.eme093

Electromagnetic Modeling

2005· other· en· W4244662841 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

VenueEncyclopedia of RF and Microwave Engineering · 2005
Typeother
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFinite-difference time-domain methodTransmission-line matrix methodMethod of moments (probability theory)Computational electromagneticsElectromagneticsElectromagnetic fieldComputer scienceDomain (mathematical analysis)Transmission lineFocus (optics)Field (mathematics)Matrix (chemical analysis)Scattering-matrix methodApplied mathematicsAlgorithmMathematicsMaxwell's equationsElectronic engineeringMathematical analysisPhysicsEngineeringTelecommunicationsOptics

Abstract

fetched live from OpenAlex

Abstract Numerical modeling of electromagnetic (EM) fields or computational electromagnetics is a combination of numerical methods and field theory. This article will focus only on some mainstream techniques, most of which employ either the method of weighted residuals or variational principles.We will begin with modeling techniques in the frequency domain, most importantly the method of moments (MoM). This section is followed by time‐domain methods and here in particular the finite‐difference time‐domain method (FDTD) and the transmission‐line matrix (TLM) method. Finally, a brief overview with respect to hybrid methods concludes this article. A comparison between the various modeling approaches as well as their advantages and disadvantages is added where appropriate.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.539
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.205
Teacher spread0.201 · 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