Effectiveness of particle swarm optimization technique in dealing with noisy data in inverse heat conduction analysis
Bibliographic record
Abstract
Three different variations of Particle Swarm Optimization (PSO) method are used to solve the inverse heat conduction problem, in one, two, and three dimensions. Both steady and transient problems are studied. Experimental results obtained from the thermocouples inside a hot plate in jet impingement problem are used as bench mark.. In this research, PSO is successfully applied to the inverse heat conduction problem, and it has alleviated some of the problems related to the instability of the classical optimization approaches. Some researches have shown that PSO can be an efficient way of solving the inverse heat conduction problem in terms of computational expense. In this research, we are mainly focused on the effect of noise in the domain, and the ability of PSO in dealing with such cases. This is very crucial, because most of the experimental engineering data is prone to some intrinsic errors in the measurements. Some ideas are proposed to make the inverse solution more robust in a noisy domain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".