Macromodel order reduction and passivity enforcement using Hamiltonian Matrix pencil perturbation
Bibliographic record
Abstract
Model Order Reduction is one of the most successful methods to minimize computational complexity in circuit simulation. Order reduction could be done as a part of the macromodeling process of frequency domain data into a highly accurate and compact time domain model. This macromodeling is done by converting the frequency domain Y-parameter or S-parameter data, which were extracted using full-wave simulation of the physical model at hand, into a descriptor state space based time domain model. Using the system identification method of Loewner Matrix, the order selection process can only be done to a certain threshold, below which a non-passive model would be produced. Such a non-passive model could become unstable when connected to other terminations, even if such terminations are stable themselves. This thesis presents a novel approach of utilizing passivity enforcement schemes, such as Hamiltonian Matrix Pencil Perturbation, to convert a system with mild passivity violation into a passive system. This would allow the macromodeling process to use an order lower than the minimum threshold, making order reduction much more efficient and effective. This whole methodology can be used to create compact time domain macromodels for the recently popular microwave applications, which otherwise would be difficult, or impossible, to find their closed form expressions using their physics based information.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".