Post-Processing of non gradient-based Topology Optimization with Simulated Annealing
Bibliographic record
Abstract
Topology optimization (TO) is a mathematical method of determining distribution of material in a design domain to achieve maximum performance for the desired application. Non-gradient-based topology optimization methods are beneficial for the problems in which the derivative of the objective function is not easy or even possible to calculate. The results coming from such methods include gray area and discontinuity based on the optimization algorithm. In this paper, a post-processing algorithm is presented to improve the results from a non-gradient topology optimization simulated annealing based process. It has been shown that the results using this post-processing method have less gray areas by fixing the densities of the elements. Therefore, better compliance values obtained for the cantilever and MBB beams problems regarding the results in the literature. The main advantage of post-processing is that the number of iterations can be reduced without sacrificing the quality of the results. This leads to improving the results as well as reducing the calculation costs by the faster convergence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".