The Barnes–Hut Hierarchical Center-of-Charge Approximation for Fast Capacitance Extraction in Multilayered Media
Bibliographic record
Abstract
The Barnes-Hut algorithm is widely used in astrophysics for solving large gravitationalN-body problems usingO(NlogN) time and memory. This reduction in computational cost is achieved by a hierarchical application of the classical center-of-mass approximation. As both gravitational and electrostatic potentials are subject to a 1/Rdependence, the Barnes-Hut algorithm is also a natural choice for rapidly evaluating interactions between charged particles. The contribution of this paper is an extension of the Barnes-Hut hierarchical clustering to the acceleration of charge interactions in stratified media. We derive and validate a closed-form expression for the shift of the center-of-charge location induced by the physical inhomogeneities and show that proper positioning of the center-of-charge ensuresO(1/R3) error decay in the field approximation. Hierarchical applications of the proposed clustering approximation is demonstrated for the construction ofO(NlogN) method-of-moment based capacitance extractors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".