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Record W1636967384 · doi:10.1109/aps.2005.1551258

Slanted walls in the FDTD method

2005· article· en· W1636967384 on OpenAlexaff
Y.S. Rickard, Natalia K. Nikolova

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFinite-difference time-domain methodGridExtrapolationComputationResonatorOffset (computer science)Perfect conductorComputer scienceBoundary value problemFinite difference methodComputational scienceMathematicsMathematical analysisAlgorithmGeometryPhysicsOptics

Abstract

fetched live from OpenAlex

In the finite-difference time-domain (FDTD) method, the spatial step is usually chosen to be between 5% and 12.5% of the minimal wavelength of interest. If the boundaries cannot be positioned at integer multiples of the chosen spatial step, one usually reduces the spatial step or uses a nonuniform grid. Reported methods for treating such offset metal boundaries require substantial changes in the FDTD code, as well as changes in the grid and the time step. Recently, we proposed an alternative method (Rickard, Y.S. and Nikolova, N.K., 2004), whereby an off-grid perfect electric conductor (PEC) and magnetic boundaries may be modeled without disturbing the original grid. The method employs extrapolation of adjacent field values from the internal computational domain to obtain exterior field values ensuring off-grid virtual boundaries in-between. As only the outer boundary values are modified, the conventional FDTD code and the grid remain unchanged. Time step reduction is unnecessary, thus the high computation speed is preserved. To validate the method, we compare the calculated frequencies of the first few resonant modes of a rectangular resonator to their analytical values when the resonator has off-grid wall(s) in parallel with and slanted to the existing grid. We investigate the accuracy the stability with off-grid BCs and give recommendations for their use.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.621

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)0.0010.000

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 head0.012
GPT teacher head0.303
Teacher spread0.290 · 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 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

Citations0
Published2005
Admission routes1
Has abstractyes

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