The Finite-Element Method Contrast Source Inversion Algorithm for 2D Transverse Electric Vectorial Problems
Bibliographic record
Abstract
The contrast source inversion algorithm is formulated using the finite-element method for two-dimensional transverse electric microwave imaging problems. Edge-based triangular elements with vector basis functions are utilized to solve the TE electromagnetic problem. A single finite-element method (FEM) mesh is used to model both the electric field as well as the contrast-source and contrast variables used in the inverse problem. The electromagnetic field is modeled by taking the unknown values to be the tangential components of the transverse electric field along the edges of each triangular element. The unknown contrast-source and contrast variables are located at the centroids of every triangular element of the same FEM mesh, but only inside the imaging domain. The adaptation of the FEM-contrast source inversion (FEM-CSI) algorithm to 2D-TE problems on such an arbitrary mesh requires the implementation of special transformation operators which are presented herein. The algorithm's capabilities are demonstrated by inverting the Fresnel experimental TE datasets as well as synthetically generated data.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".