Reconstruct permeability of heterogeneous body using T-Ω 3-D formulation and Gauss-Newton's method with total variation regularization
Bibliographic record
Abstract
In the inverse problems several techniques are used, in particular induced currents techniques. In spite of their complexities, a diversity of fields, such as imaging, use these techniques. Owing to the fact that it is not a direct problem, several conditions must be joined together to give satisfactory results. The choice of the Model, the Numerical Technique, the Iterative Method and the choice of Regularization Method are the factors which influence the exactitude and the convergence of the results. In this paper, we are going to determine a permeability distribution of heterogeneous body, from a nondestructive evaluation signal, using the numerical approach. We will solve the forward solution with a three-Dimensional T-Ω formulation of Finite Element Method in order to determine the magnetic field, and a Gauss-Newton algorithm with Total Variation Regularization to solve and stabilize the solution resulting from the optimisation problem. The permeability of the object is assumed to be linear and isotropic. Results for the permeability imaging will be reconstructed using synthetically generated data.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".