Discretization of the Poisson equation using the interpolating scaling function with applications
Bibliographic record
Abstract
Dyadic translations of the interpolating scaling function generate a basis that can be used to approximate functions and develop a multiresolution methodology for constructing smooth surfaces or curves. Many wavelet methods for solving partial di erential equations are also derived from the interpolating scaling function. However, little is done for developing a higher order numerical discretization methodology using the scaling function. In this article, we have employed an iterative interpolation scheme for the construction of scaling functions in a twodimensional mesh that is a finite collection of rectangles. We have studied the development of a weighted residual collocation method for approximating partial derivatives. We show that the discretization error is controlled by the order of the scaling function. The potential of this novel technique has been verified with some representative examples of the Poisson equation. We have extended the technique for solving nonlinear advection-di usion equations, and simulated a shear driven flow in a square cavity at CFL = 2:5 (Courant Friedrichs Lewy) andRe = 1 000 (Reynolds number). Agreement with the reference solution at a large CFL = 2:5 explores the potential of this development for advection dominated problems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".