MétaCan
Menu
Back to cohort
Record W3037412743 · doi:10.1002/mmce.20454

The state of the art of microwave CAD: EM-based optimization and modeling

2010· article· en· W3037412743 on OpenAlexaff
Qingsha S. Cheng, J.W. Bandler, Sławomir Kozieł, Mohamed H. Bakr, Stanislav Ogurtsov

Bibliographic record

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCADElectromagneticsMicrowaveSpace mappingImplementationComputer scienceElectronic engineeringComputational electromagneticsComputer engineeringSensitivity (control systems)Engineering optimizationSystems engineeringOptimization problemEngineeringTelecommunicationsAlgorithmEngineering drawingElectromagnetic fieldSoftware engineeringPhysics

Abstract

fetched live from OpenAlex

We briefly review the current state of the art of microwave CAD technologies. We look into the history of design optimization and CAD-oriented modeling of microwave circuits as well as electromagnetics-based optimization techniques. We emphasize certain direct approaches that utilize efficient sensitivity evaluations as well as surrogate-based optimization approaches that greatly enhance electromagnetics-based optimization performance. On the one hand, we review recent adjoint methodologies, on the other we focus on space mapping implementations, including the original, aggressive, implicit, output, tuning, and related developments. We illustrate our presentation with suitable examples and applications. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.192
Teacher spread0.187 · 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
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

Citations37
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of RF and Microwave Computer-Aided EngineeringSame topicMicrowave Engineering and WaveguidesFrench-language works237,207