Advanced Mesh Space Mapping Approach With Fast Coarse Mesh Models Comprising Sharpening Structural Processing and Mesh Deformation
Bibliographic record
Abstract
Mesh space mapping (MSM) is widely recognized as a popular surrogate-based optimization approach for expediting electromagnetic (EM) design, particularly in cases where traditional equivalent circuit coarse models are not readily available for standard space mapping (SM). This article proposes an advanced MSM method incorporating fast coarse mesh models. A sharpening structural processing (SSP) technique is introduced for a fine model consisting of curved elements to generate a coarse model composed of entirely sharp-cutting structures. As a result, coarser meshes can be utilized in the coarse model, significantly reducing costly and time-consuming computations. Furthermore, optimization of the coarse mesh incorporates an improved mesh deformation technique, enabling continuous variation of the EM responses with respect to changing geometric dimensions. The synergistic combination of the SSP and mesh deformation techniques yields a considerably low-computational-cost coarse mesh model, accelerating the overall MSM optimization. Three examples of EM optimization of microwave components demonstrate the proposed method.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".