Theory, analytical investigation, and performance of the complementary derivatives method for reducing reflection errors from nonuniform grid domains in finite difference methods
Bibliographic record
Abstract
The central finite difference is used very often to approximate first‐order differential equations, and it results in a second‐order truncation error for a uniform grid size. Nonuniform grids are used for simulating structures with large aspect ratios or problems with large field gradients in order to improve computational efficiency. However, changing the grid size increases the truncation error at the interface between domains having different grid sizes. The error at the interface is manifested as a spurious reflection from the grid boundary, thus decreasing the simulation accuracy. The complementary derivatives method (CDM) was originally introduced as a robust discretization technique to eliminate any spurious errors arising from the changing grid sizes. In this paper, we review the theory of the CDM. We investigate the CDM analytically for the one‐dimensional case and derive the fundamental modes of propagation in the numerical solution of the differential equation. Then, we calculate the reflection coefficient from the interface of two domains having different grid sizes with and without the CDM. Different representative numerical examples also demonstrate the efficiency of the CDM in reducing the reflection from the grid boundary and improving the simulation results in different applications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".