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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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.638

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 head0.007
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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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