MétaCan
Menu
Back to cohort
Record W2069610665 · doi:10.1364/josaa.26.001915

Finite-element solution of the problem of scattering from cavities in metallic screens using the surface integral equation as a boundary constraint

2009· article· en· W2069610665 on OpenAlexafffund
Babak Alavikia, Omar M. Ramahi

Bibliographic record

VenueJournal of the Optical Society of America A · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntegral equationFinite element methodMathematical analysisScatteringBoundary element methodSurface (topology)Boundary value problemBoundary (topology)Matrix (chemical analysis)Domain (mathematical analysis)Bounded functionPhysicsMathematicsOpticsGeometryMaterials science

Abstract

fetched live from OpenAlex

This work presents a novel finite-element solution to the problem of scattering from multiple two-dimensional cavities in infinite metallic walls. The technique presented here is highly efficient in terms of computing resources and is versatile and accurate in comparison with previously published methods. The formulation is based on using the surface integral equation with the free-space Green's function as the boundary constraint. The solution space is divided into local bounded frames containing each cavity. The finite-element formulation is applied inside each frame to derive a linear system of equations associated with nodal field values. The surface integral equation is then applied at the opening of the cavities to truncate the computational domain and to connect the matrix subsystem generated from each cavity. The near and far fields are generated for different single and multiple cavity examples. The results are in close agreement with methods published earlier.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.254
Teacher spread0.238 · 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
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

Citations25
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the Optical Society of America ASame topicElectromagnetic Scattering and AnalysisFrench-language works237,207