SMF: A User-Friendly Software Engine for Space-Mapping-Based Engineering Design Optimization
Bibliographic record
Abstract
Although space mapping is a powerful optimization and modeling methodology, it is not always straightforward to implement, especially if one wants to use some advanced techniques and employ commercial simulators in the automatic optimization loop. The SMF system is a user-friendly space mapping software that is designed to make space mapping accessible to engineers inexperienced in this technology. SMF allows automatic or interactive space-mapping-based constrained optimization, modeling and statistical analysis. It incorporates most of the existing space mapping techniques including input, output, implicit, and frequency space mapping as well as the latest developments such as space-mapping based interpolation and advanced modeling techniques. SMF provides sockets to popular EM/circuit simulators including Sonnet em, MEFiSTo, ADS, and FEKO. In this paper we give a brief introduction to space mapping methodology as well as an exposition of the SMF system with special focus on its optimization interfaces. An SMF application example is given.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.052 | 0.012 |
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".