Multiorder Arnoldi Approach for Model Order Reduction of PEEC Models With Retardation
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Bibliographic record
Abstract
This paper presents an efficient algorithm to create reduced-order models of large linear networks that contain delay elements. The proposed algorithm is based on a multiorder Arnoldi algorithm used to implicitly calculate the moments with respect to frequency. This procedure generates reduced-order models that preserve the structure of the original system, without having to introduce any extra state variables to calculate the moments. In addition, it is shown that the orthonormal subspace of the system, built by introducing extra state variables, is embedded in the subspace constructed by the multiorder Arnoldi approach. Numerical examples of distributed interconnects modeled by partial element equivalent circuits that include retardation effects are described to illustrate the validity of the proposed reduction 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