MultiAIM: Fast Electromagnetic Analysis of Multiscale Structures Using Boundary Element Methods
Bibliographic record
Abstract
Integral equation methods are extensively used for computational electromagnetism, and can be applied to large problems when accelerated with fast multipole or fast Fourier transform (FFT) techniques. Unfortunately, the efficiency of FFT-based acceleration schemes can be dramatically reduced by the presence of multiscale features. Large triangles will impose a relatively coarse mesh, and large regions where FFT must be replaced by integration. Since many small triangles can fall in this region, integration costs will become prohibitive, diminishing the benefits provided by FFT. We propose an efficient and robust algorithm to overcome this barrier, based on multigrid concept. A hierarchy of grids of different resolution is used to simultaneously resolve subwavelength details and propagate fields efficiently across large distances with the FFT. Integration and precorrection costs are minimized by adapting projection stencils to the size of each triangle and enabling the use of the quasi-static Green’s function for short distances. Finally, a clever implementation based on sparse matrices exploits empty areas to reduce computational cost and memory consumption. The method is fully automated, and was tested on several structures including layouts of commercial products. Compared to existing adaptive integral method (AIM) algorithms, we demonstrate a speed-up between 7.1 and$24.7\times $and a reduction in memory consumption by up to 2.9 times.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".