An Algorithmic Approach to Formulate Well-Conditioned Stable Reduced-Order Models of Active Circuits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A model order reduction technique was introduced to preserve the stability of large full-order models and efficiently handle the complexity of large stable active circuits. The key principle of this approach was to ensure that the reduced model satisfies the Lyapunov equation, thereby guaranteeing stability at the time of construction. However, this method relied on an oblique projection operation involving two distinct bases applied to the large matrices of the full-order model. While the oblique projection theoretically preserved stability and maintained the accuracy of the reduced model, it often led to numerical anomalies that caused simulation failures. This paper presents an algorithmic approach designed to detect and, when necessary, correct numerical ill-conditioning in the matrices generated by oblique projection. Numerical simulations validate the robustness of the proposed method by demonstrating its effectiveness in restoring the accuracy of models that would have, otherwise, yielded erroneous simulation results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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