Parallel Multiphysics Optimization for Microwave Devices Exploiting Neural Network Surrogate
Bibliographic record
Abstract
This letter proposes a new surrogate-based multiphysics optimization technique for microwave devices incorporating artificial neural networks (ANNs) and trust-region algorithm. In the proposed technique, at each optimization iteration, we build an accurate and efficient ANN surrogate model using multiple multiphysics training samples around the optimized solution from the previous iteration. A parallel data generation technique is exploited to accelerate the optimization process. To improve the convergence of the proposed technique, we use a trust-region algorithm to recalculate the ANN surrogate model range at each optimization iteration. By using the proposed technique, the values of design parameters have a large and effective update toward the optimal solution at each iteration, and the optimization can converge in fewer iterations. Therefore, we can achieve the optimal solution faster than existing multiphysics optimization methods. A waveguide filter using piezo actuator is used as an example to demonstrate the validity of our technique.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".