Error-controlled boundary element modeling of 3D plasmonic nano-structures via higher-order Locally Corrected Nystrom method
Bibliographic record
Abstract
Traditional low order numerical method of computational electromagnetics produce notably higher error when applied to analysis of plasmonic nano-structures compared to the structures with conventional values of permittivity and permeability. The high error levels are typically observed at the junctions of the low-order elements on the surface of such structures. It is caused by the artificial geometrical discontinuities resulted from flat panelled approximation of the physically smooth surface. Increase of the low-order discretization density typically does not reduce such error effectively. In this work we describe higher-order boundary element modelling approach which eliminates such errors and provides error-controlled approximation of the fields in arbitrary smooth 3D structures down to machine precision if necessary. The approach is based on higher-order Locally Corrected Nystrom discretization of the traditional surface Electric Field and Magnetic Field integral equations formulated for multi-region penetrable objects.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".