MétaCan
Menu
Back to cohort
Record W2117367253 · doi:10.1109/tmtt.2005.862663

Sensitivity analysis of network parameters with electromagnetic frequency-domain simulators

2006· article· en· W2117367253 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2006
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSensitivity (control systems)SolverFEKOComputationComputer scienceOverhead (engineering)Frequency domainComputational electromagneticsElectromagneticsComputational scienceFinite difference methodAlgorithmElectronic engineeringMathematicsSoftwareElectromagnetic fieldMathematical analysisEngineeringPhysics

Abstract

fetched live from OpenAlex

A new practical approach to sensitivity analysis of the network parameters of high-frequency structures with commercial full-wave electromagnetic (EM) solvers is proposed. We show that the computation of the linear-network parameter derivatives in the design-parameter space does not require an adjoint-problem solution. The sensitivities are computed outside the EM solver, which simplifies the implementation. We discuss: 1) features of commercial EM solvers which allow the user to compute network parameters and their sensitivities through a single full-wave simulation; 2) the accuracy of the computed derivatives; and 3) the overhead of the sensitivity computation. Through examples based on FEMLAB and FEKO simulations, comparisons are made with the forward finite-difference derivative estimates in terms of accuracy and CPU time.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.413
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.217
Teacher spread0.213 · 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