Disordered Dynamic Systems Resulting From Approximate Modeling and Analysis
Bibliographic record
Abstract
Abstract Some dynamic systems exhibit curve veering behavior or avoided crossings when their natural frequencies are plotted against a system parameter, while some other show a curve crossing behavior or frequency coalescence. The curve veering behavior is also observed in disordered systems where the symmetry of the system is slightly perturbed and a mode localization takes place. In some systems while the exact analysis shows a curve crossing trend, approximate analyses show a curve veering behavior. Earlier studies have shown that there is a common pattern in curve veering systems and disordered systems. In the present study the exact analysis is recognized as representing the actual system while the approximate analysis of the same system renders it a disordered system by perturbing the eigenvalues and eigenfunctions from their true values. Since the responses of disordered systems can sometimes show violent changes for small perturbations in the system parameters, the response of a simply supported plate has been obtained both exactly and approximately using the Rayleigh Ritz method, and compared. The conclusions have far reaching implications from the point of the accuracy of the response quantities obtained by approximate methods such as finite element method, the Rayleigh Ritz and Galerkin methods.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".