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Record W1497469375 · doi:10.1109/epeps.2014.7103642

Non-intrusive pseudo spectral approach for stochastic macromodeling of EM systems using deterministic full-wave solvers

2014· article· en· W1497469375 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
FieldDecision Sciences
TopicProbabilistic and Robust Engineering Design
Canadian institutionsMcGill University
Fundersnot available
KeywordsRobustness (evolution)Computer scienceInterpolation (computer graphics)Stochastic processPolynomial chaosAlgorithmMathematical optimizationStochastic modellingGalerkin methodSpectral methodMathematicsMonte Carlo methodFinite element methodEngineeringTelecommunications

Abstract

fetched live from OpenAlex

In this paper, a novel stochastic macromodeling technique for the variability analysis of complex electromagnetic (EM) structures is proposed. This work combines a pseudo spectral approach with the Loewner matrix interpolation technique to generate the polynomial chaos macromodel from the stochastic S-parameters of the structure. The major benefit of the proposed strategy is that by exploiting the non-intrusive nature of the pseudo spectral approach, the stochastic macromodel can be generated directly from a small number of deterministic EM full-wave simulations. This enables the utilization of the robustness and versatility of conventional deterministic full-wave techniques without the need for the cumbersome stochastic Galerkin formulation.

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)0.002
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: none
Teacher disagreement score0.752
Threshold uncertainty score0.736

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.111
GPT teacher head0.314
Teacher spread0.204 · 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