MétaCan
Menu
Back to cohort
Record W2795564931 · doi:10.1109/epeps.2017.8329725

Acceleration of shielding effectiveness analysis using stable FDTD subgridding

2017· article· en· W2795564931 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFinite-difference time-domain methodElectromagnetic shieldingComputer scienceAccelerationEnclosureStability (learning theory)GridFinite difference methodElectromagnetic fieldElectromagnetic compatibilityElectronic engineeringEngineeringMathematicsElectrical engineeringPhysicsTelecommunicationsOpticsMathematical analysisMachine learningGeometry

Abstract

fetched live from OpenAlex

Even though it is desirable to fully shield computer components from external electromagnetic field, this cannot be done due to the need for ventilation. The finite-difference time-domain (FDTD) method can be used to analyze the shielding effectiveness of a computer enclosure with apertures. The multiscale nature of this problem calls for the use of a locally refined grid (subgridding), which however can compromise FDTD stability. We present a systematic approach to create provably-stable FDTD subgridding schemes and investigate their ability to accelerate the assessment of shielding effectiveness.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.283

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 head0.042
GPT teacher head0.339
Teacher spread0.297 · 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