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 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 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".