MétaCan
Menu
Back to cohort
Record W2752764391 · doi:10.1109/tmag.2017.2748100

An Efficient Implementation of the Classical Preisach Model

2017· article· en· W2752764391 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.

Bibliographic record

VenueIEEE Transactions on Magnetics · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsPreisach model of hysteresisComputationComputer scienceConvergence (economics)HysteresisFinite element methodMagnetic hysteresisRange (aeronautics)Function (biology)Applied mathematicsAlgorithmPhysicsMathematicsMagnetizationMaterials scienceMagnetic fieldThermodynamics

Abstract

fetched live from OpenAlex

The incorporation of hysteresis models in the finite-element (FE) method is important for the accurate predictions of the performance of low-frequency electromagnetic devices. The Jiles-Atherton and Preisach models are frequently used for this purpose. The classical Preisach model is more accurate and can represent a broad range of magnetic materials. However, it is computationally very expensive and therefore hysteresis-coupled FE simulations take too much time to solve. In this paper, a computationally efficient method of implementing the Preisach model is presented using the closed-form expression for modeling the Everett function, which not only reduces the total execution time of the model but also simplifies its implementation. The incorporation of the proposed implementation into FE simulations shows faster computation times and better numerical convergence when compared to the conventional implementation. The proposed approach is only valid for the H-based Preisach models.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.621

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.304
Teacher spread0.275 · 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