Bibliographic record
Abstract
This thesis analyses numerical methods used in finding solutions of diferential equations. Numerical methods are viewed as discrete dynamical systems that give useful information on continuous dynamical systems defined by systems of (ordinary) diferential equations. We analyse non-standard finite difference schemes that have no spurious fixed-points compared to the dynamical system under consideration, the linear stability/instability property of the fixed-points being the same for both the discrete and continuous systems. We obtain a sharper condition for the elementary stability of the schemes. For more complex dynamical systems which are dissipative, we design schemes that replicate this property. Furthermore, we investigate the impact of the above analysis on the numerical solution of partial differential equations. We specifically focus on reaction-diffusion equations that arise in many fields of engineering and applied sciences. Often their solutions enjoy the follow- ing essential properties: Stability/instability of the fixed points for the space independent equation, the conservation of energy for the stationary equation, and boundedness and positivity. We design new non-standard finite diference schemes which replicate these properties. Our construction make use of three strategies: the renormalization of the denominator of the discrete derivative, non-local approximation of the nonlinear terms and simple functional relation between step sizes. Numerical results that support the theory are provided. Copyright
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".