Systèmes hamiltoniens à ports de dimension infinie. Réduction et propriétés spectrales
Bibliographic record
Abstract
Dans ce papier on s'intéresse aux propriétés spectrales des systèmes hamiltoniens à ports de dimension infinie. On montre que le spectre de tels systèmes peut être déduit du spectre associé à une structure canonique, la structure de Stokes-Dirac, à l'aide de transformations géométriques simples telles que des homothéties, translations ou dilatations. On montrera en particulier que les spectres des systèmes d'équations d'onde et de diffusion peuvent être déduits du même spectre canonique. Enfin on étudiera les propriétés de convergence d'une méthode de réduction structurée de type éléments finis mixtes sur une problème de diffusion. Pour cela on montrera la convergence du spectre de la structure réduite vers le spectre de la structure canonique de Stokes-Dirac, puis on en déduira les propriétés de convergence finale par transformation géométrique. ABSTRACT. This paper deals with spectral properties of infinite dimensional Port Hamiltonian systems. It is shown that the spectra of these systems can be deduced from the spectrum associated to a canonical structure, the Stokes Dirac structure, thanks to geometric transformations such as homothety, translations or dilations. In particular, it is shown that the spectrum of the wave equation system and of the diffusion system can be deduced from the same canonical spectrum. Finally the spectral convergence properties of a mixed finite element based spatial reduction methods is studied of a diffusion system. To this purpose, the convergence of the spectrum of the reduced structure to the spectrum of the canonical Stoke Dirac structure is proved. It is obtained from the convergence properties of the canonical structure through geometric transformations.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".