MétaCan
Menu
Back to cohort
Record W3148216306 · doi:10.1109/tcpmt.2021.3071063

Fast and Stable Time-Domain Simulation Based on Modified Numerical Inversion of the Laplace Transform

2021· article· en· W3148216306 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsUniversity of OttawaCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaplace transformInversion (geology)Time domainLaplace transform applied to differential equationsPost's inversion formulaComputer scienceAlgorithmLaplace–Stieltjes transformMellin transformInverse Laplace transformTwo-sided Laplace transformComputer simulationMathematical optimizationApplied mathematicsSimulationMathematicsMathematical analysisGeologyFourier transformGreen's function for the three-variable Laplace equationFractional Fourier transformComputer vision

Abstract

fetched live from OpenAlex

This article presents a new algorithm for time-domain circuit simulation based on numerical inversion of Laplace transform (NILT). The proposed method, labeled NILT n, shows that for the same computational cost of the conventional NILT, referred to as NILT0, the approximation error is reduced by a significant factor, permitting time-marching with much larger time steps and, consequently, saving significant computational cost per time step. Numerical experiments are presented to demonstrate the efficiency and accuracy of the proposed approach.

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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.519

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)0.0000.000

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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