MétaCan
Menu
Back to cohort
Record W1917753824 · doi:10.1109/aps.1999.789335

Neural network structures for EM/microwave modeling

2003· article· en· W1917753824 on OpenAlexaff
Q.J. Zhang, Fei Wang, V. Devabhaktuni

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicNeural Networks and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsArtificial neural networkMicrowaveComputer scienceMicrowave engineeringTime delay neural networkArtificial intelligenceElectronic engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

Neural networks have gained attention as a fast and flexible vehicle to EM/microwave modeling, simulation and optimization. Neural network device/circuit models can be used during EM/microwave design to provide instant answers. The structure of the neural network influences factors such as training data and training time required, and the accuracy that could be achieved. This paper discusses various microwave-oriented neural network structures.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.268

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 head0.029
GPT teacher head0.258
Teacher spread0.229 · 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 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

Citations7
Published2003
Admission routes1
Has abstractyes

Explore more

Same topicNeural Networks and ApplicationsFrench-language works237,207