High-order contrast source inversion of dielectric targets using a Discontinuous Galerkin discretization of the vector wave equation
Bibliographic record
Abstract
The Finite-Element Method Contrast Source Inversion (FEM-CSI) algorithm is a versatile tool for solving electromagnetic imaging problems. Among the benefits of FEM-CSI are its ability to handle imaging system boundaries and inhomogeneous background media without requiring a numerical Green's function. Our more recent work has focused on extending the forward solver used in CSI to high-order by replacing the FEM formulation with a time-harmonic Discontinuous Galerkin Method (DGM) discretization of Maxwell's curl equations. Due to its high-order capabilities, DGM-CSI effectively decouples the contrast and field discretizations without introducing a dual mesh. The drawback of DGM-CSI based on Maxwell's curl equations is that it requires solving for both the electric and magnetic fields simultaneously, even when magnetic fields are not available in the measurement data. In this work we present CSI that uses a DGM discretization of the electric vector wave equation (VWE-DGM-CSI). This approach requires less time and memory than its counterpart based on the curl equations. If measurement data includes magnetic fields, the high-order electric field solution can be converted to magnetic fields using differential operators.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".