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Record W2111353132 · doi:10.1109/mwsym.2001.967290

Numerical cost of gradient computation within the method of moments and its reduction by means of a novel boundary-layer concept

2002· article· en· W2111353132 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsFLOPSComputationSensitivity (control systems)Moment (physics)Reduction (mathematics)Boundary (topology)Computer scienceMatrix (chemical analysis)Simple (philosophy)AlgorithmFloating pointMethod of moments (probability theory)Numerical analysisBoundary layerApplied mathematicsMathematicsMathematical optimizationMathematical analysisParallel computingGeometryElectronic engineeringPhysicsEngineeringMechanicsStatistics

Abstract

fetched live from OpenAlex

A rigorous investigation of the numerical cost of sensitivity analysis (gradient computation) of complex structures within moment method is presented. It is shown that, when the number of variables N used in the analysis is large, a common situation in complex structures, the ratio r of the number of flops required to evaluate the sensitivity of the response to structural changes to the number of flops required to determine the response at a single point is such that r=0(1/N) as long as the number of flops required to fill the matrix is not dominant. For the latter important case, a new boundary layer concept is introduced to reduce the CPU time for the gradient computation. A simple example of an iris in a rectangular waveguide is used to illustrate the concept and show its validity.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.246

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 head0.034
GPT teacher head0.304
Teacher spread0.270 · 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