Model-Order Reduction of Massively Coupled Parameterized Systems via Clustering
Bibliographic record
Abstract
Parametrized Model Order Reduction(PMOR) methods reduce the full equations for large parametric networks into a reduced number of parametrized equations. The reduced model is obtained to match the variations in the response of the original full circuit due to variations in its key design parameters. Applying existing PMOR techniques to parametrized systems with many inputs often results in extremely large and dense reduced-order models. This thesis presents a new approach to construct parametrized reduced-order models for multi-parameter networks with many input/output terminals. The new method leverages the efficient application of the multi-parameter moment-matching-based approach by exploiting the superposition paradigm. Solving the resulting overall reduced system to obtain variations in the original circuit response yields significant computational savings. The presented algorithm is an effective means to manage the Monte Carlo simulations of large multiport parametrized linear networks. It is highly suited for multithreading implementation and thus facilitates parallel time-domain variability analysis.
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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) | 0.001 | 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) | 0.001 | 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".