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Record W2107726372 · doi:10.1109/tap.2008.928789

An Improved New Implementation of Complex Frequency Shifted PML for the FDTD Method

2008· article· en· W2107726372 on OpenAlexfundno aff
Antonios Giannopoulos

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2008
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsnot available
FundersUniversity of EdinburghYork University
KeywordsPerfectly matched layerFinite-difference time-domain methodSimple (philosophy)Computer scienceComputational electromagneticsBoundary (topology)AlgorithmFunction (biology)Finite difference methodComputational complexity theoryBoundary value problemComputational scienceMathematicsMathematical analysisElectromagnetic fieldOpticsPhysics

Abstract

fetched live from OpenAlex

A new implementation of the perfectly matched layer absorbing boundary for finite-difference time-domain grids is presented. The approach which is based on the complex co-ordinate stretching perfectly matched layer (PML) formulation uses the complex frequency shifted stretching function and is based on the simple concept of the recursive evaluation of an integral avoiding the calculation of time derivatives. This recursive integration PML is simple to implement, efficient and exhibits a modest gain in performance over the convolutional PML without requiring any extra computational resources or an increase in the algorithmic complexity of the PML implementation.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.039
GPT teacher head0.329
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 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

Citations41
Published2008
Admission routes1
Has abstractyes

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