Massively Parallel Computation for 3-D Nonlinear Finite Edge Element Problem With Transmission Line Decoupling Technique
Bibliographic record
Abstract
Transmission line method (TLM) has been used in 2-D scalar finite-element (FE) analysis due to its parallelism and constant admittance matrix. In this paper, the TLM is extended for the 3-D nonlinear vector FE problem that is more widely used for electromagnetic apparatus in practice. TLM is specially adapted for tetrahedron edge elements to calculate quasi-static electromagnetic field distribution for eddy current problems, and a dummy scalar gauge is applied to make the reduced magnetic vector FE formulation full-ranked and uniquely solvable by TLM. For each element, the nonlinearity is separated by transmission lines and only local small-scale Newton–Rapson iteration is needed, which is suitable for massive parallelization due to the independence between different elements. The TLM is implemented on a many-core GPU for a nonlinear FE power inductor case study, and the comparison of the results with a commercial FE software shows over 50 times speedup with a relative error of less than 2%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".