Convergence improvement in finite difference solution using MC and DC methods for magnetic field analysis
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Bibliographic record
Abstract
Among the numerical methods used in the electromagnetic modeling and simulation of electrical systems, the iterative method is included. In this paper, different techniques are employed to a classical Gauss-Seidel Algorithm. It used to improve accuracy and convergence of the solutions for a common partial differential equation in finite difference method. The first method is named Double Convergence Method in which combinations of two Iteration Methods with different initial points are utilized. The second method is called Multi-level Convergence Method where, a mixture of multi Iteration Method with initial values obtained from previous processes using first, second, third order polynomial for the next round of iteration. The convergence time and accuracy of both methods are evaluated and compared using classical Gauss-Seidel Algorithm by solving various one dimensional partial differential equations. The aim of this paper is to reduce the number of iterations of this method in order to reduce the computing time and to improve the convergence speed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it