MétaCan
Menu
Back to cohort
Record W2027361961 · doi:10.1109/epeps.2012.6457889

Waveform relaxation with overlapping based partitioning for fast transient simulation of package/board power distribution networks

2012· article· en· W2027361961 on OpenAlexaff
Sourajeet Roy, Anestis Dounavis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Noise Suppression
Canadian institutionsWestern University
Fundersnot available
KeywordsWaveformDiscretizationRelaxation (psychology)Convergence (economics)Computer scienceTransient (computer programming)Power (physics)AlgorithmIterative methodPower integrityPartial element equivalent circuitNode (physics)Topology (electrical circuits)Electronic engineeringSignal integrityEquivalent circuitMathematicsPhysicsEngineeringElectrical engineeringTelecommunicationsMathematical analysisAcoustics

Abstract

fetched live from OpenAlex

Modeling of power distribution networks in electronic packages requires the two dimensional discretization of the distributed power/ground planes which can be computationally expensive. Recently, the waveform relaxation algorithm has been proposed for fast transient simulation of power/ground planes. However, due to the strong coupling of each node in a two dimensional (2D) physical space, the relaxation iterations exhibit slow convergence and special techniques need to be adopted to ensure efficient convergence. In this work, a novel waveform relaxation algorithm based on physically partitioning the power/ground plane into smaller overlapping subcircuits is presented. The overlap between the subcircuits provides greater exchange of information per iteration leading to accelerated convergence of the waveform relaxation algorithm. A numerical example has been provided to illustrate the validity of the proposed algorithm over full SPICE simulations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.525
Threshold uncertainty score0.353

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 head0.211
Teacher spread0.203 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicElectromagnetic Compatibility and Noise SuppressionFrench-language works237,207