Advanced Model-Order Reduction Techniques for Large Scale Dynamical Systems
Bibliographic record
Abstract
Model Order Reduction (MOR) has proven to be a powerful and necessary tool for various applications such as circuit simulation.In the context of MOR, there are some unaddressed issues that prevent its efficient application, such as "reduction of multiport networks" and "optimal order estimation" for both linear and nonlinear circuits.This thesis presents the solutions for these obstacles to ensure successful model reduction of large-scale linear and nonlinear systems.This thesis proposes a novel algorithm for creating efficient reduced-order macromodels from multiport linear systems (e.g.massively coupled interconnect structures).The new algorithm addresses the difficulties associated with the reduction of networks with large numbers of input/output terminals, that often result in large and dense reduced-order models.The application of the proposed reduction algorithm leads to reduced-order models that are sparse and block-diagonal in nature.It does not assume any correlation between the responses at ports; and thereby overcomes the accuracy degradation that is normally associated with the existing (Singular Value Decomposition based) terminal reduction techniques.Estimating an optimal order for the reduced linear models is of crucial importance to ensure accurate and efficient transient behavior.Order determination is known to be a challenging task and is often based on heuristics.Guided by geometrical considerations, a novel and efficient algorithm is presented to determine the minimum sufficient order that ensures the First and foremost, I would sincerely like to express my gratitude to my supervisor, Professor Michel Nakhla.Without his guidance, this thesis would have been impossible.I appreciate his insight into numerous aspects of numerical simulation and circuit theory, as well as his enthusiasm, wisdom, care and attention.I have learned from him many aspects of science and life.Working with him was truly an invaluable experience.I am also sincerely grateful to to my co-supervisor, Professor Ram Achar, for his helpful suggestions and guidance, which was crucial in many stages of the research for this thesis.Most of all I wish to thank him for his motivation and encouragements.I would like to thank my current and past fellow colleagues in our Computer-Aided Design group for keeping a spirit of collaboration and mutual respect.They were always readily available for some friendly deliberations that made my graduate life enjoyable.I will always fondly remember their support and friendship.I am thankful towards the
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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 | 0.000 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".