Solution of large multiscale EMC problems with method of moments accelerated via low-frequency MLFMA
Bibliographic record
Abstract
The paper demonstrates that for large-scale electromagnetic compatibility problems not exceeding 120λ in size the low-frequency-multilevel-fast-multipole-algorithm (LF-MLFMA) based on spherical wave function expansions is advantageous to its high-frequency counterpart based on plane wave expansions. In the latter the depth of the tree is restricted by the smallest size of the leaf-level box size of 0.1λ making it inefficient at either low-frequencies or for problems with multi-scale features. The low-frequency MLFMA, however, has no limitation on the depth of the tree and allows for full-wave acceleration of Moment Method from DC to frequencies at which the models spans up to 120λ. Such broadband behaviour of the low-frequency MLFMA is made possible through construction of numerically stable translation operators for the spherical wave functions with orders reaching 180. This paper provides an overview of the algorithms allowing for stable high order translations of spherical functions. The LF-MLFMA accelerated Rao-Wilton-Glisson Moment Method utilizing one such algorithm is demonstrated in the frequency range from 1MHz to 2.5GHz for the problem of plane wave coupling to antennas onboard the F5 fighter jet at fixed discretization featuring 2 million surface elements.
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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) | 0.002 | 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".