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Record W2982235669 · doi:10.1109/iceaa.2019.8879055

H-Matrix Fast Direct Solution of Surface-Volume-Surface EFIE for Scattering Problems on General Composite Metal-Dielectric Objects

2019· article· en· W2982235669 on OpenAlexaff
Reza Gholami, Vladimir Okhmatovski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDiscretizationElectric-field integral equationIntegral equationDielectricMathematical analysisScatteringMethod of moments (probability theory)Surface (topology)MathematicsBoundary value problemBoundary (topology)PhysicsGeometryOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

Solution of radiation and scattering problems on metal-dielectric composite objects plays an important role in remote sensing, antenna design, and various other areas. We recently developed formulation of the Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE) [1] to solution of composite dielectric scatterers [2]. In this work we generalize the SVS-EFIE formulation to the case of piece-wise homogeneous scatterers which feature both penetrable dielectric regions and impenetrable metal regions. Independent electric surface current density is introduced on the boundary of each region forming the composite object. In addition, each common boundary between distinct regions of the scatterer features two independent unknown surface currents. This offers two advantages when it comes to numerical solution of the SVS-EFIE in comparison to the numerical solution of the classical surface integral equation formulations such as PMCHWT, Muller, or others. First, this independence of the unknown currents eliminates the problem of the current discretization at the material junctions. Second, the boundaries of the regions can be meshed independently and in correspondence with the material properties of their respective region which makes the proposed method more efficient for multiscale problems.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.895

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2019
Admission routes1
Has abstractyes

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