Analysis of Iterative Sub-structuring Techniques for Boundary Element Approximation of the Hypersingular Operator in Three Dimensions
Bibliographic record
Abstract
The article deals with the analysis of Additive Schwarz preconditioners for the h -version of the boundary element method for the hypersingular integral equation on surfaces in three dimensions. The first preconditioner consists of decomposing into local spaces associated with the subdomain interiors, supplemented with a wirebasket space associated with the subdomain interfaces. The wirebasket correction only involves the inversion of a diagonal matrix, while the interior correction consists of inverting the sub-blocks of the stiffness matrix corresponding to the interior degrees of freedom on each subdomain. It is shown that the condition number of the preconditioned system grows at most as max K H m 1 (1 + log H / h K ) 2 where H is the size of the quasi-uniform subdomains and h K is the size of the elements in subdomain K . A second preconditioner is given that incorporates a coarse space associated with the subdomains. This improves the robustness of the method with respect to the number of subdomains: theoretical analysis shows that growth of the condition number of the preconditioned system is now bounded by max K (1 + log H / h K ) 2 .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".