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Fast Direct Method of Moments Solution of Surface-Volume-Surface Integral Equation with H-Matrices

2018· article· en· W2892924322 on OpenAlexaff
Reza Gholami, Jamiu Mojolagbe, Anton Menshov, Farhad Sheikh Hosseini Lori, Vladimir Okhmatovski

Bibliographic record

Venue2018 2nd URSI Atlantic Radio Science Meeting (AT-RASC) · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectric-field integral equationDiscretizationIntegral equationSurface (topology)Method of moments (probability theory)Volume integralMathematicsMathematical analysisScatteringSurface integralGeometryPhysicsOptics

Abstract

fetched live from OpenAlex

The Surface-Volume-Surface Electric Field Integral Equation (SVS-EFIE) [1] is a single-source integral equation which can be formulated for solution of radiation and scattering problems on homogeneous as well as piece-wise homogeneous (composite) penetrable objects. The Method of Moments (MoM) discretization of SVS-EFIE produces three dense matrices corresponding to its three integral operators. These operators map the field from the scatterer's surface to its volume, from its volume to its surface, and from its surface to back its surface. Because of the discretization of both the surface and the volume of the scatterer the resultant dense matrices take large amount of memory and require prolonged computational time, if handled directly. In this work we demonstrate a computational framework based on the theory of hierarchical matrices (H-matrices) [2], which allows to greatly alleviate the CPU time and memory complexity of the SVS-EFIE MoM solution.

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)0.002
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 score0.408
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.011
GPT teacher head0.257
Teacher spread0.246 · 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
Published2018
Admission routes1
Has abstractyes

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