The application of Trefftz-FLAME to electromagnetic wave problems /
Bibliographic record
Abstract
Numerical analysis of the electromagnetic fields in large, complex structures is very challenging due to the high computational overhead. Recently, it has been shown that a new method called Trefftz-FLAME ( Flexible Local Approximation MEthod) is suitable for problems where there exist a large number of similar structures. This thesis develops Trefftz-FLAME in two areas. First, a novel 2D Trefftz-FLAME method incorporates the modal analysis and port boundary condition that are essential to an accurate calculation of reflection and transmission coefficients for photonic crystal devices. The new technique outperforms existing methods in both accuracy and computational cost. The second area pertains to the 3D, vector problem of electromagnetic wave scattering by aggregates of identical dielectric particles. A methodology for the development of local basis functions is introduced, applicable to particles of any shape and composition. Boundary conditions on the surface of the finite FLAME domain are described, capable of representing the incident wave and absorbing the outgoing radiation. A series of problems involving dielectric spheres is solved to validate the new method. Comparison with exact solutions is possible in some cases and shows that the method is able to produce accurate near-field results even when the computational grid spacing is equal to the radius of the spheres.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".