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Record W2169321175 · doi:10.1002/nme.4969

Algorithmically imposed thermodynamic compliance for material models in mechanical simulations using the AIM method

2015· article· en· W2169321175 on OpenAlexaff
Cyrille F. Dunant, Evan C. Bentz

Bibliographic record

VenueInternational Journal for Numerical Methods in Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsConvergence (economics)Work (physics)Range (aeronautics)Path (computing)Process (computing)Computer scienceApplied mathematicsStatistical physicsMechanical engineeringMathematicsEngineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

Summary Thermodynamic irreversibility can be imposed on empirical material behaviour by using an appropriate algorithm which takes the path‐dependence of the degradation process into account. This new algorithm, Algorithmically Imposed Mechanics (AIM), for algorithmically irreversible mechanics, is described, and the convergence and unicity of the solutions obtained are proven. AIM is applicable to a range of mechanical behaviour and is demonstrated to work in conjunction with non‐local damage with rotating cracks as well as a mixed plastic and damage behaviour. Copyright © 2015 John Wiley & Sons, Ltd.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.113
GPT teacher head0.441
Teacher spread0.328 · 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.

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

Citations11
Published2015
Admission routes1
Has abstractyes

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