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Record W4388739649 · doi:10.23977/jeis.2023.080502

Acceleration of Radiation Analysis Using an Arbitrary High Order Difference Method with Non-uniform Mesh

2023· article· en· W4388739649 on OpenAlexvenueno aff
Jun Li, Qun Zhang, Peng Jiang, Aote Zhang

Bibliographic record

VenueJournal of Electronics and Information Science · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsnot available
FundersStrong
KeywordsComputer scienceComputationGridLimit (mathematics)AccelerationComputational scienceAlgorithmScale (ratio)Time domainWork (physics)Mathematical optimizationMathematicsGeometryMathematical analysisPhysicsEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

For the analysis of irregular structures, an arbitrary high-order finite difference time-domain local time step based on non-uniform grids is proposed. Comparison with Finite Difference Time Domain, this new method based on non-uniform mesh weak the limit of the time-step size. For the new method allows different grids to interate with different time-step sizes. In order to perform fast and accurate electromagnetic analysis on the responsible problem, it is necessary to use multi-scale grids to segment the target. Using appropriate time steps to solve electromagnetic fields on different grids, achieving accurate and fast analysis of problems. Due to breaking through the limitation of grid size on time step size, the computational workload of the work is reduced. In addition, multi-time steps are implemented by local time step and an arbitrary high order is added to this work to promise the accuracy of computation. This work has great value in practical application engineering.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.010
GPT teacher head0.279
Teacher spread0.269 · 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
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
Published2023
Admission routes1
Has abstractyes

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