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Record W2005638858 · doi:10.1109/tcad.2014.2364974

Formulation of the Obreshkov-Based Transient Circuit Simulator in the Presence of Nonlinear Memory Elements

2014· article· en· W2005638858 on OpenAlexaff
Yaoyao Lin, Emad Gad

Bibliographic record

VenueIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSpeedupNonlinear systemSpiceTransient (computer programming)Computer scienceElectronic circuitElectronic circuit simulationDomain (mathematical analysis)Transient responseTime domainTransient analysisElectronic engineeringControl theory (sociology)Parallel computingMathematicsEngineeringMathematical analysisPhysicsArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

The high-order A- and L-stable Obreshkov-based method was recently proposed for simulating the transient response of general nonlinear circuits in the time-domain. This method has consistently resulted in more than one order-of-magnitude speedup compared with the traditional methods used in the SPICE engine. Nonetheless, the formulation of this Obreshkov-based approach assumed that the memory elements in the circuits are characterized by a linear constituent equation. The goal of this paper is to generalize this approach to handle memory elements that are described by nonlinear constituent equations.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.217
Teacher spread0.195 · 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

Citations6
Published2014
Admission routes1
Has abstractyes

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