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Record W2137824199 · doi:10.1002/jnm.1872

Reduced‐cost microwave component modeling using space mapping‐enhanced electromagnetic‐based kriging surrogates

2012· article· en· W2137824199 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

VenueInternational Journal of Numerical Modelling Electronic Networks Devices and Fields · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSpace mappingDiscretizationMicrowaveInterpolation (computer graphics)KrigingMicrostripComponent (thermodynamics)Computer sciencePlanarFidelityElectronic engineeringAcousticsAlgorithmPhysicsMathematicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

ABSTRACT We present a versatile technique for constructing fast microwave component models at a low computational cost. Our modeling procedure consists of two stages: (i) kriging interpolation of coarsely discretized electromagnetic simulation data of the device under consideration and (ii) enhancing the kriging model by using space mapping and a limited amount of high‐fidelity electromagnetic simulation data. The use of coarse discretization data allows for low‐cost model generation, whereas space mapping is a convenient way of ensuring its accuracy. Our approach can be applied to almost any type of microwave device; it can also work with other types of nonsmooth or costly low‐fidelity models. The operation and performance of our methodology is demonstrated using a variety of microwave components, including a microstrip filter, a planar ultrawideband antenna, and a microstrip‐to‐coplanar waveguide transition. Copyright © 2012 John Wiley & Sons, Ltd.

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 score0.817
Threshold uncertainty score0.942

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 integrity0.0000.001
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 head0.018
GPT teacher head0.237
Teacher spread0.219 · 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