Multilevel Reduced-Order Coarse-Model Development Technique for Accelerating Space Mapping Optimization of Microwave Filters
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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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".