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Record W3211412245 · doi:10.1016/j.ifacol.2021.08.078

Topological Optimization and Simulated Annealing

2021· article· en· W3211412245 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

VenueIFAC-PapersOnLine · 2021
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsSimulated annealingTopology optimizationDiscontinuity (linguistics)MinificationCrystallizationTopology (electrical circuits)Annealing (glass)Mathematical optimizationConvergence (economics)CantileverComputer scienceDerivative (finance)AlgorithmMathematicsMaterials scienceMathematical analysisPhysicsFinite element methodEngineeringStructural engineering

Abstract

fetched live from OpenAlex

In this paper, a new non-gradient-based topology optimization (TO) method proposed. Simulated annealing (SA) with crystallization factor used to generate new solutions. During this process, the newly generated solutions evaluated based on the SA concept. A density filter also applied to remove the discontinuity of shapes. The innovation of this method is applying the history of accepted or rejected solutions by the crystallization factor. Results of compliance minimization of cantilever and MBB-beams from the proposed method compared with the results of gradient-based methods. The main advantage of the proposed method is the improvement of convergence of the results as well as no need for the derivative of the objective function.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.702

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 head0.007
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