Global Optimization Using Mixed Surrogate Models for Computation Intensive Designs
Bibliographic record
Abstract
Despite of today’s steady and continuing improvement of computation power, effective use of complex and computational intensive engineering analysis and simulation codes in design optimization remains a challenge. In this work, a new global optimization algorithm, namely Mixed Surrogate Models and Design Space Elimination Search (MSMDSES), is introduced. The approach divides the field of interest into several unimodal regions; identify and rank the regions that likely contain the global minimum; fits a Radial Basis function and Quadratic Response Surface model over each promising region with additional design experiments data points using Latin Hypercube designs; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The new algorithm was tested using several benchmark problems for global optimization and compared with several widely used region elimination and space exploration global optimization algorithms, showing reduced computation efforts, robust performance and comparable search accuracy, making the new method an excellent tool for computation intensive, computer analysis/simulation based global design optimization problems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".