MétaCan
Menu
Back to cohort
Record W1997615575 · doi:10.1061/40794(179)19

A Framework for Simulation-Based Optimization with Application to Green Building Design

2005· article· en· W1997615575 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Multi-Objective Optimization Algorithms
Canadian institutionsÉcole de Technologie SupérieureConcordia University
Fundersnot available
KeywordsComputer scienceMathematical optimizationEngineering optimizationOptimization problemContinuous optimizationGenetic algorithmMultidisciplinary design optimizationSimulation-based optimizationTopology optimizationMulti-swarm optimizationAlgorithmEngineeringMathematicsMachine learningFinite element method

Abstract

fetched live from OpenAlex

This paper presents an object-oriented framework that gives attention to the features of simulation-based optimization problems such as hierarchical variables and the coupling with simulation programs. The framework consists of three basic modules: the variables module, the simulation module, and the optimizer module. The variables module defines variables and organizes them according to their relationships. The simulation module evaluates objective functions and functional constraints. The optimizer module implements optimization algorithms. The genetic algorithms implemented in the optimizer module can solve (1) unconstrained single objective optimization problems, (2) constrained single objective optimization problems, and (3) unconstrained multi-objective optimization problems. The application of this framework is demonstrated through a case study for green building design.

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 score0.064
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.023
GPT teacher head0.312
Teacher spread0.289 · 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