A Subregion Expansion Method for Computational Electromagnetics
Bibliographic record
Abstract
This paper presents a new semi-analytical method for computational electromagnetics. Today, the usual electromagnetic field simulation codes are mainly based on the finite element method (FEM). FEM uses the piecewise low order polynomials to approximate different kind of solution functions. The particular nature of a given problem is not considered, which greatly enhances the method's applicability but depresses the efficiency. In this method, the entire field domain has to be covered with fine mesh, especially in those parts where the field changes sharply, thus requiring a large number of elements (and also unknowns) to calculate the field with sufficient precision. This often makes the codes useless due to the computation cost (CPU time and memory) before some very complex problems. To overcome these limitations, quick and highly efficient techniques are pursued all along. The semi-analytical method is such a direction. The main idea of this scheme is to combine the advantages of both the high efficiency of analytical techniques and the flexibility of numerical methods.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".