Novel high precision UFFT methodology for fast analysis of 3D Planar circuits embedded in shielded layered media
Bibliographic record
Abstract
Unified-FFT (UFFT) is a novel 3D-Planar Method of Moments (MoM) solver of the Electric Field Integral Equation (EFIE). UFFT is the resultant algorithm of combining the FFT-enhanced Matrix Solve Operations (MSO) currently utilized in Sonnet Suites with FFT-enhanced Matrix Fill Operations (MFO). It has been shown, in conjunction with iterative MSO based on GMRES, to solve matrix vector products scaling with O(NlogN) operations and O(N) memory for planar, single-plane geometries (B. J. Rautio, V. I. Okhmatovski, J.K. Lee, IMS 2013). Previous UFFT implementations have achieved O(NlogN) scaling through acceleration of iterative matrix-vector products within GMRES by separating near and far interactions and calculating far interactions implicitly as in PFFT. In this work, the same uniform grid used with MFO is extended for use with MSO, negating the need to treat near and far interactions independently and allowing the entire matrix to be stored implicitly with no significant loss of precision. The resulting algorithm, UFFT-Grid Totalizing (UFFT-GT), achieves O(NlogN) operations and O(N) memory scaling with minimal loss in precision vs. full matrix inverse beyond numerical noise.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".