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A High-Order FEM Solution for a Time-Domain Electromagnetic Geophysical Forward Solver: Preliminary Results

2025· article· en· W4413322108 on OpenAlexafffund
Amirmasoud Amirkabiri, Lucas Banting, Ian Jeffrey, Colin Gilmore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Simulation and Numerical Methods
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolverFinite element methodComputational electromagneticsDomain (mathematical analysis)ElectromagneticsTime domainGeophysicsComputer scienceOrder (exchange)Applied mathematicsComputational scienceGeologyMathematical analysisElectromagnetic fieldPhysicsMathematicsMathematical optimizationEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

In this paper, we present a high-order finite element method (FEM) solution for time-domain electromagnetic (TDEM) forward modeling, optimized for geophysical applications. The solver utilizes unstructured tetrahedral meshes and Gaussian-distributed current sources instead of conventional point sources, improving numerical accuracy and reducing mesh refinement requirements. The method employs higher-order basis functions to achieve accurate transient responses with fewer degrees of freedom, enhancing computational efficiency. A variable time-stepping scheme is implemented to balance accuracy and performance, achieving convergence within an error threshold 3% compared to analytical solutions.

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: Methods
Teacher disagreement score0.918
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.247
Teacher spread0.241 · 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
Published2025
Admission routes2
Has abstractyes

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