Efficient design optimization of microwave circuits using parallel computational methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An efficient design optimization technique is proposed for microwave circuits using multi-point function evaluations in each iteration to achieve a large effective optimization update. In this technique, a local model is developed in the neighborhood of the nominal point for each iteration. EM data for building the local model is optimally chosen using design of experiments method. Further, the EM data is generated by running multiple EM simulations in parallel on a multi-processor environment. Hence, we can afford to develop a local model for a wider neighborhood by increasing the number of EM simulations and still keep the computation time low. Once developed, the local model allows a large and effective optimization update in the wider neighborhood of the nominal point, thereby resulting in fewer optimization iterations and achieving speedup for the optimization process. Two typical band-pass filter examples are used to illustrate 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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