Grid refinement and scaling for distributed parameter estimation problems
Bibliographic record
Abstract
This paper considers problems of distributed parameter estimation from data measurements on solutions of differential equations. A nonlinear least squares functional is minimized to approximately recover the sought parameter function (i.e. the model). This functional consists of a data fitting term, involving the solution of a finite volume or finite element discretization of the forward differential equation, and a Tikhonov-type regularization term, involving the discretization of a mix of model derivatives. The resulting nonlinear optimization problems can be very large and costly to solve. Thus, we seek ways to solve as much of the problem as possible on coarse grids. We propose to search for the regularization parameter first on a coarse grid. Then, a gradual refinement technique to find both the forward and inverse solutions on finer grids is developed. The grid spacing of the model discretization, as well as the relative weight of the entire regularization term, affect the sort of regularization achieved and the algorithm for gradual grid refinement. We thus investigate a number of questions which arise regarding their relationship, including the correct scaling of the regularization matrix. For nonuniform grids we rigorously associate the practice of using unscaled regularization matrices with approximations of a weighted regularization functional. We also discuss interpolation for grid refinement. Our results are demonstrated numerically using synthetic examples in one and three dimensions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".