Numerical study of the modeling error in the online input estimation algorithm used for inverse heat conduction problems (IHCPs)
Bibliographic record
Abstract
A numerical investigation has been conducted to study the effect of modeling error in the state equation on the performance of the online input estimation algorithm in its application to the inverse heat conduction problems. This modeling error is used as a tuning parameter known as the stabilizing parameter in the online input estimation algorithm of the inverse heat conduction problems. Three different cases which cover most forms of the boundary heat flux functions have been considered. These cases are: square wave, triangular wave and mixed wave heat fluxes. The investigation has been carried for a one dimensional inverse heat conduction problem. Temperature measurements required for the inverse algorithm was generated by using a numerical solution of the direct heat conduction problem employing the three boundary heat flux functions. The most important finding of this investigation is that a robust range of the stabilizing parameter has been found which achieves the desired trade-off between the filter tracking ability and its sensitivity to measurement errors. For all three considered cases, it has been found that there is a common optimal value of the stabilizing parameter at which the estimate bias is minimal. This finding is very important for practical applications since this parameter is unknown practically and this study provides a needed guidance for assuming this parameter. The effect of changing other important parameters in the online input estimation algorithm on its performance has also been studied in this investigation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".