Parametric Model Order Reduction Technique For Design Optimization
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Bibliographic record
Abstract
Model order reduction has proven to be an effective tool for dealing with the computational complexity that arises during the simulation of large interconnect networks. However, in the case of parametric reduced order models, the effectiveness of traditional reduction methods is dependent on the number of moments and cross moments required to construct the orthonormal basis used in the congruence transformation. This can result in a relatively large reduced system in cases when the number of parameters is large. We propose a new approach for constructing the orthonormal basis that is not directly dependent on the moments. This new technique reduces a circuit with respect to many parameters by using singular value decomposition as a tool to filter out redundant information from the original subspaces. The result is a parametric reduced order model that is smaller, but still conserves the essential behavior of the original circuit as a function of frequency and other circuit parameters.
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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 | 0.001 |
| 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