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Record W2144553815 · doi:10.1109/mwsym.2003.1210938

Feasible adjoint sensitivity technique for EM design exploiting Broyden's update

2003· article· en· W2144553815 on OpenAlexaff
Reza Safian, Natalia K. Nikolova, Mohamed H. Bakr, J.W. Bandler

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSensitivity (control systems)Overhead (engineering)Frequency domainAntenna (radio)Computer scienceMatrix (chemical analysis)Adjoint equationMathematical optimizationAlgorithmMathematicsApplied mathematicsControl theory (sociology)Electronic engineeringMathematical analysisEngineering

Abstract

fetched live from OpenAlex

The EM-FAST feasible adjoint sensitivity technique has been proposed for use with frequency domain electromagnetic solvers. It employs finite differences to approximate the derivatives of the system matrix with respect to the design parameters. Here, we propose to estimate and update these derivatives by the classical Broyden technique. This significantly accelerates the response sensitivity analysis when EM-FAST is used for gradient-based optimization. Regardless of the number of design parameters, the response sensitivity is obtained with. computational overhead negligible in comparison with the system analysis. Our EM-Broyden technique is illustrated through the optimization of a Yagi-Uda antenna.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.854
Threshold uncertainty score0.527

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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
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

Citations5
Published2003
Admission routes1
Has abstractyes

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