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Record W1989731987 · doi:10.1049/ip-map:20045103

Adaptive plane-wave expansion algorithm for efficient computation of electromagnetic fields in low-frequency-problems

2006· article· en· W1989731987 on OpenAlexaff
M. Ayatollahi, S. Safavi‐Naeini

Bibliographic record

VenueIEE Proceedings - Microwaves Antennas and Propagation · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputationAlgorithmMathematicsPlane waveMatrix (chemical analysis)Multiplication (music)Computational complexity theoryMethod of moments (probability theory)Plane (geometry)Iterative methodFunction (biology)Mathematical analysisMathematical optimizationPhysicsGeometryOptics

Abstract

fetched live from OpenAlex

An algorithm is presented for efficient computation of electromagnetic interactions between a large number of sources in electrically small problems. The algorithm is based on a plane-wave expansion of the free-space Green's function. The expansion consists of both propagating and evanescent plane waves, and is stable at low frequencies. The algorithm is used in the iterative solution procedure of the method of moments to reduce the computational complexity of solving the matrix equation. It reduces the complexity of the matrix–vector multiplication from O(N2) to O(N log N). The numerical results verify the validity and efficiency of the algorithm in solving large-scale and low-frequency problems.

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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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