New Adaptive Multi-Expansion Frequencies Approach for SP-MORe Techniques With Application to the Well-Conditioned Asymptotic Waveform Evaluation
Bibliographic record
Abstract
Fast frequency sweep using model order reduction (MORe) techniques is one of the most valuable and efficient features in high-frequency simulators. These techniques usually use a single full solution at a given frequency to build a smaller approximation reduced model, which might be insufficient for wideband and ultrawideband simulations of high quality factor RF and microwave devices. Despite the fact that many computationally reliable error estimate methods have been presented, they all share the same starting point, which is to estimate the residual error between fast sweep solutions and not the actual error. In this paper, we introduce a new accurate and computationally reliable error estimate approach based on the lossless network condition for the automation of single-point MORe. When applied with the well-conditioned asymptotic waveform evaluation, the new approach shows very good performances in terms of accuracy and computation time compared with existing multipoint MORe and commercial EM software.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".