Multilevel Reduced-Order Coarse-Model Development Technique for Accelerating Space Mapping Optimization of Microwave Filters
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Bibliographic record
Abstract
Electromagnetic (EM) optimization of microwave components often relies on repeated full-wave simulations, which can be time-consuming even for routine design tasks. Existing acceleration techniques such as mesh space mapping (MSM) typically require manual coarse-mesh tuning and coarse–fine fitting, limiting automation and reproducibility. To address these issues, this article presents a multilevel model order reduction (MOR) framework that derives a reduced-order coarse model (ROCM) directly from the fine model, thereby avoiding mesh coarsening and empirical fitting. The first level enables rapid broadband evaluation via frequency-domain reduction; the second reuses a shared projection basis across nearby geometries with adaptive updates when surrogate error grows. Geometry changes are accommodated through mesh deformation, and sensitivities are computed efficiently by restricting adjoint-based updates to perturbed elements. The proposed method is validated on multiple waveguide filter designs and compared against direct fine-mesh optimization, single-level MOR, and MSM. Across the reported cases, the proposed method achieves the shortest total runtime while maintaining fine-model accuracy and stable convergence, eliminating the need for coarse-mesh construction and surrogate fitting. These results demonstrate a robust, automated workflow that combines high fidelity with substantial computational savings over practical design ranges.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it