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 the variations in its key design parameters.Applying the 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.By clustering the full equations into I would like to extend my sincere thanks and appreciation to my supervisor, Professor Michel Nakhla, who constantly pushed me to be professional and do the right thing despite the challenges I encountered.Without his persisting help, the task of completing this thesis could never have been accomplished.I would also like to express my sincere gratitude and appreciation to my cosupervisor Dr. Behzad Nouri.Having encouraging words and thoughtful, detailed feedback from you has been very important to me.I am particularly grateful for the time you have taken out of your schedules to complete this research and make this project possible.I am fortunate to have been a part of the CAD group.Thank you so much for your support through thick and thin.With such an organized, open, and helpful support system, every task, regardless of its difficulty, feels achievable.Finally, I want to extend my sincere gratitude to my family for their tremendous support and hope
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".