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Record W4413318959 · doi:10.1109/mmm.2025.3567411

Mesh Space Mapping Technique for Microwave Filter Design

2025· article· en· W4413318959 on OpenAlexaff
Mutian Li, Feng Feng, Jiali Zhang, Jinyi Liu, Jingpei Zhang, Ke Liu, Qi‐Jun Zhang

Bibliographic record

VenueIEEE Microwave Magazine · 2025
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsCarleton University
Fundersnot available
KeywordsSpace mappingMicrowaveElectronic engineeringFilter (signal processing)Computer scienceSpace (punctuation)Waveguide filterMicrowave transmissionPrototype filterMaterials scienceFilter designEngineeringTelecommunicationsComputer vision

Abstract

fetched live from OpenAlex

This article focuses on the issues of high computational resource requirements and high time costs in the design of microwave filters using traditional electromagnetic (EM) simulation methods, and presents a detailed introduction to the mesh space mapping (MSM) technique. MSM is based on the assumption of coarse and fine models and improves the optimization efficiency by establishing a mapping relationship between them. The article elaborates on its general concept and implementation process, with a particular emphasis on the MSM method integrating mesh morphing, sharpening processing, and structure simplification. Verified by examples, the MSM based on sharpening structural processing (SSP) converges faster, and the structure - simplified MSM can obtain the optimal design solution in the shortest time. MSM, combined with low - cost coarse - mesh modeling, effectively accelerates the design optimization of microwave components. In the future, its combination with advanced technologies is expected to further enhance the efficiency of EM modeling and design.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.285
Threshold uncertainty score1.000

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 head0.225
Teacher spread0.206 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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