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Record W2905530452 · doi:10.1109/antem.2018.8573005

Solution Convergence in Exterior Electromagnetic Boundary Value Problems and Its Dependence on the Numerical Methods-a Review

2018· article· en· W2905530452 on OpenAlexaff
L. Shafai, Navid Rezazadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBoundary value problemBoundary (topology)UniquenessMathematicsMathematical analysisConvergence (economics)Mixed boundary conditionField (mathematics)Free boundary problemMethod of fundamental solutionsSingular boundary methodElectromagnetic fieldBoundary element methodFinite element methodPhysics

Abstract

fetched live from OpenAlex

In numerical solution of electromagnetic problems, boundary conditions play an essential role to ensure the uniqueness of the solution. The problem may be solved by a variety of methods, such as the integral or differential equation methods, a modal expansion method, or some random application of boundary conditions on the object. In any case, the application of boundary conditions is used to determine the unknown field quantity of the formulation to be used for determining the entire solution in the desired space. The convergence of the solution, therefore, depends on the selection of the unknown and the method used for its determination. However, in exterior boundary value problems the field quantities of interest are at far distances from the boundaries, where the field behaviors are dependent on the entire boundary of the object. Thus, the behaviour of the far field vectors is different from those on the boundary. This problem is investigated here by means of simple scattering by conducting objects having simple, as well as, complex shapes. It is shown that, while the convergence of the solution for determining the boundary fields can be slow, or difficult to obtain, depending on the shape of the object, the convergence of the far field solution is usually more rapid. In particular, the convergence of the far field is mostly dependent on the size of the object, rather than its shape.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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.

Study designSimulation or modeling
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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