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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".