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Record W4309354484 · doi:10.1002/9781119808404.ch3

The Finite‐Element Time‐Domain Method for Dispersive and Nonlinear Media

2022· other· en· W4309354484 on OpenAlexaff
David S. Abraham, Ali Akbarzadeh‐Sharbaf, Dennis D. Giannacopoulos

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsMcGill University
Fundersnot available
KeywordsNonlinear systemFinite element methodCurl (programming language)Time domainMaxwell's equationsApplied mathematicsStability (learning theory)Dispersive partial differential equationMathematical analysisDomain (mathematical analysis)MathematicsComputer sciencePhysicsPartial differential equation

Abstract

fetched live from OpenAlex

Several finite-element time-domain formulations capable of solving complex electromagnetic problems in nonlinear and dispersive media are presented in this chapter. Two types of formulations are considered, which are based on the vector wave equation and the coupled first-order Maxwell curl equations. This chapter starts with the definition of dispersive and nonlinear media, their common models and modeling techniques, followed by a derivation of the standard finite-element time-domain formulations. In the following sections, the formulations are extended to handle general combination of material dispersion and nonlinearity. Lastly, stability of the formulations has been studied. Implementation issues and various numerical examples are also presented throughout the chapter.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.187
Threshold uncertainty score0.987

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)0.0140.000

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 head0.008
GPT teacher head0.274
Teacher spread0.266 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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