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Record W2888400569 · doi:10.1109/icgpr.2018.8441622

Weighted-averaging operators for accurate 2.5D finite-difference frequency domain radar waves modeling

2018· article· en· W2888400569 on OpenAlexaff
B. Doyon, Bernard Giroux

Bibliographic record

Venue2018 17th International Conference on Ground Penetrating Radar (GPR) · 2018
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité LavalCenter for Northern Studies
Fundersnot available
KeywordsStencilFrequency domainAlgorithmComputer scienceComputational electromagneticsFinite difference methodGridRadarRegular gridNumerical analysisMathematical optimizationApplied mathematicsMathematicsMathematical analysisPhysicsElectromagnetic fieldGeometryTelecommunicationsComputational science

Abstract

fetched live from OpenAlex

We propose an algorithm to accurately solve the frequency domain electromagnetic wave equation by finite-differences in 2.5D. The algorithm is based on previously published methods for viscoelastic waves, and relies on a nine-point stencil to build weighted-averaging numerical operators. The weights are chosen to minimize numerical dispersion and anisotropy, which allows relaxing the requirements on grid cell size and thus decreases computational costs. This new algorithm reduces the numerical error compared to central finite-difference method without increasing the numerical bandwidth of the matrix system to solve, and can be easily transposed to 3D frequency domain modeling.

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)0.002
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.059
GPT teacher head0.314
Teacher spread0.255 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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