Multi-Objective Design of Compact Microwave Components with Data-Driven Surrogates and Pareto Front Decomposition
Bibliographic record
Abstract
The paper discusses low-cost multi-objective optimization of compact microwave components using variablefidelity EM simulation models and data-driven surrogates. Our approach builds upon a recently reported method where the initial approximation of the Pareto set is obtained by optimizing the kriging surrogate constructed from sampled data of the coarsediscretization EM model of the structure at hand, with selected designs further refined to obtain the high-fidelity Pareto set. The drawback of the method is a large number of training data samples required to set up the surrogate. Here, considerable savings concerning the training data set size are achieved by Pareto front decomposition based on auxiliary points identified along the front and setting up the kriging models in the corresponding subdomains. The key factor is that the total volume of the sub-domains is considerably smaller than the volume of the original domain. Our considerations are illustrated using a compact rat-race coupler with design optimization cost savings of 29- and 30-percent for two and three sub-domains, respectively.
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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 | 0.001 |
| 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) | 0.001 | 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".