A Residual Smoothing Strategy for Accelerating Newton Method\n Continuation
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Bibliographic record
Abstract
A technique for accelerating global convergence of pseudo-transient\ncontinuation Newton methods is proposed based on residual smoothing. The\ntechnique is motivated by the effectiveness of local nonlinear smoothers at\novercoming strong nonlinear transients. In the limit of a small pseudo-time\nstep, the method reduces to a local nonlinear smoothing technique, while in the\nlimit of large pseudo-time steps, an exact Newton method is recovered along\nwith its quadratic convergence properties. The formulation relies on the\naddition of a smoothing source term while leaving the Newton Jacobian matrix\nunchanged, thus simplifying implementations for existing Newton solvers. The\nproposed technique is demonstrated on a steady-state and an implicit\ntime-dependent computational fluid dynamics problem, showing significant gains\nin overall solution efficiency.\n
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it