Bibliographic record
Abstract
For nonlinear circuits, DC analysis is one of the circuit simulation applications.The conventional source stepping continuation method is computationally expensive to do the Newton-Raphson iteration due to the large numbers of nonlinear functions and the inversion of a large matrix.It is also hard to find an efficient algorithm to do the Model Order Reduction (MOR), DC analysis and the parameterized analysis for nonlinear systems.To address the above difficulties, a novel method is proposed in this thesis for the DC analysis and the parameterized analysis.This new approach reduces the computational cost by applying the model order reduction technique to the original model.The model size of the reduced order model is much smaller, thus the computational time will be saved during the Newton-Raphson iteration.In addition, the proposed method solves the MOR problem for the nonlinear vector by applying the DEIM algorithm.Furthermore, the format of the DC solution parameterized analysis is provided.To the end, basing on the new circuit equation format, a method to compute the moment subspace is presented after applying the DEIM approximation.Pertinent numerical results are presented to verify the proposed algorithm.The simulation result also demonstrates the efficiency of the new method to solve the nonlinear equations. J(x)Jacobian function ζ Jacobian function of the circuit parameter F k Kth moment of the nonlinear function J k Kth moment of the Jacobian function U projection base of the nonlinear function Γ nonlinear function interpolation indices P DEIM selection matrix xiv
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".