Delay extraction and passive macromodeling of lossy coupled transmission lines
Bibliographic record
Abstract
Recently, several algorithms were proposed for time-domain macromodeling of distributed transmission line networks. It has been demonstrated that preserving passivity of the macromodel is essential to guarantee a stable global transient simulation. Techniques such as method-of-characteristics yield fast transient results for long delay lines. However, they do not guarantee the passivity of the macromodel. On the other hand, methods such as matrix rational approximation provide efficient macromodels for lossy coupled lines, while preserving passivity. However, for long lossy delay lines this may require higher order approximations, making the macromodel inefficient. In order to address the above difficulty, this paper presents a new algorithm for efficient macromodeling of lossy coupled lines with long delay. The proposed method employs delay extraction prior to approximating the exponential stamp of the line and guarantees the macromodel passivity. The paper also provides guidelines on the practical applicability of the delay extraction and the matrix rational approximation, based on the knowledge of line parameters.
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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".