Loewner Matrix Macromodeling for Y-Parameter Data With a Priori $\textbf {D}$ Matrix Extraction
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
Macromodeling techniques using Loewner matrix (LM) interpolation were proposed recently as a way to generate time-domain macromodels based on simulated V-parameters. These approaches scale very well with respect to the number of ports as well as the number of poles in the system. However, these methods become less efficient in terms of accuracy and passivity for V-parameters obtained using electromagnetic simulators. In this paper, we propose an LM-based interpolation technique that is applicable for large-scale distributed systems described by full-wave V-parameters. An algorithm to approximate and extract the port impedance matrix D directly from the data is proposed. Additionally, an order selection scheme is proposed that results in an accurate macromodel while maintaining passivity. The efficiency and accuracy of the proposed approach is illustrated using comparisons with a standard technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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