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Record W2047679457 · doi:10.1109/tap.2014.2365047

Algebraic Multigrid Combined With Domain Decomposition for the Finite Element Analysis of Large Scattering Problems

2014· article· en· W2047679457 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

VenueIEEE Transactions on Antennas and Propagation · 2014
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsMultigrid methodDomain decomposition methodsFinite element methodScatteringAlgebraic equationMatrix (chemical analysis)Matrix decompositionMathematical analysisPlane waveApplied mathematicsMathematicsComputer sciencePhysicsPartial differential equationOpticsMaterials scienceEigenvalues and eigenvectorsQuantum mechanics

Abstract

fetched live from OpenAlex

In the finite element analysis of electromagnetic scattering, a recent algebraic multigrid method (Aghabarati and Webb, “An Algebraic Multigrid Method for the Finite Element Analysis of Large Scattering Problems,” IEEE Trans. Antennas Propag., vol. 61, no, 2, pp. 809-817, Feb. 2013) has been shown to be an efficient way to solve the large, sparse matrix equation. However, the effectiveness of the method decreases as the electrical size of the problem increases. This limitation is overcome by combining it with a domain decomposition method (DDM). Exact solution on each domain is not needed: a single W-cycle of the multigrid method is sufficient. Results are presented for scattering of plane waves by conducting and dielectric objects, with dimensions ranging from 3 wavelengths to 20 wavelengths.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.275

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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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