EM-Centric Multiphysics Optimization of Microwave Components Using Parallel Computational Approach
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Bibliographic record
Abstract
For the high-performance microwave component and system design, besides electromagnetic (EM) physics domain, we also need to consider the operation of the real-world multiphysics (MP) environment that contains the effects of other physics domains. EM-centric MP analysis and design optimization become very important. In this article, for the first time, we develop a novel parallel EM-centric multiphysics optimization (MPO) technique. In our proposed technique, the pole/residue-based transfer function is exploited to build an effective and robust surrogate model. A group of modified quadratic mapping functions is formulated to map the relationships between pole/residues of the transfer function and the design variables. Multiple EM-centric MP evaluations are performed in parallel to generate the training samples for establishing the surrogate model. Using our proposed technique, the surrogate model can be valid in a relatively large neighborhood, which makes an effective and large optimization update in each optimization iteration. The trust region algorithm is performed to guarantee the convergence of the proposed MPO algorithm. Our proposed MPO technique takes a small number of iterations to obtain the optimal EM-centric MP response. Two microwave filter examples are used to demonstrate the validity of the proposed technique.
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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 | 0.000 | 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 | 0.000 |
| 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 it